Overview

Brought to you by YData

Dataset statistics

Number of variables52
Number of observations7058
Missing cells207575
Missing cells (%)56.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory416.0 B

Variable types

Numeric26
Categorical19
Text5
Boolean2

Alerts

Process (PMF) - Prototyping Used has constant value "True" Constant
- CASE Tool Used is highly overall correlated with Process (PMF) - Development Methodologies and 4 other fieldsHigh correlation
External (EEF) - Data Quality Rating is highly overall correlated with Process (PMF) - Development Methodologies and 4 other fieldsHigh correlation
People (PRF) - BA team experience 1 to 3 yr is highly overall correlated with People (PRF) - IT experience 1 to 3 yr and 6 other fieldsHigh correlation
People (PRF) - BA team experience <1 yr is highly overall correlated with People (PRF) - IT experience <1 yr and 2 other fieldsHigh correlation
People (PRF) - BA team experience >3 yr is highly overall correlated with People (PRF) - IT experience >3 yr and 3 other fieldsHigh correlation
People (PRF) - IT experience 1 to 3 yr is highly overall correlated with People (PRF) - BA team experience 1 to 3 yr and 9 other fieldsHigh correlation
People (PRF) - IT experience 3 to 9 yr is highly overall correlated with Project (PRF) - Currency multiple and 2 other fieldsHigh correlation
People (PRF) - IT experience <1 yr is highly overall correlated with People (PRF) - BA team experience <1 yr and 4 other fieldsHigh correlation
People (PRF) - IT experience <3 yr is highly overall correlated with Process (PMF) - Development Methodologies and 2 other fieldsHigh correlation
People (PRF) - IT experience >3 yr is highly overall correlated with People (PRF) - BA team experience >3 yr and 10 other fieldsHigh correlation
People (PRF) - IT experience >9 yr is highly overall correlated with People (PRF) - BA team experience >3 yr and 3 other fieldsHigh correlation
People (PRF) - Personnel changes is highly overall correlated with Process (PMF) - Development Methodologies and 1 other fieldsHigh correlation
People (PRF) - Project manage changes is highly overall correlated with Project (PRF) - Cost currency and 1 other fieldsHigh correlation
People (PRF) - Project manage experience is highly overall correlated with Project (PRF) - Currency multiple and 2 other fieldsHigh correlation
People (PRF) - Project user involvement is highly overall correlated with Process (PMF) - Development Methodologies and 6 other fieldsHigh correlation
Process (PMF) - Development Methodologies is highly overall correlated with - CASE Tool Used and 13 other fieldsHigh correlation
Process (PMF) - Docs is highly overall correlated with - CASE Tool Used and 9 other fieldsHigh correlation
Project (PRF) - Cost currency is highly overall correlated with People (PRF) - Project manage changes and 5 other fieldsHigh correlation
Project (PRF) - Currency multiple is highly overall correlated with People (PRF) - BA team experience 1 to 3 yr and 10 other fieldsHigh correlation
Project (PRF) - Defect Density is highly overall correlated with Project (PRF) - Currency multiple and 2 other fieldsHigh correlation
Project (PRF) - Functional Size is highly overall correlated with Project (PRF) - Manpower Delivery Rate and 5 other fieldsHigh correlation
Project (PRF) - Manpower Delivery Rate is highly overall correlated with People (PRF) - IT experience >3 yr and 8 other fieldsHigh correlation
Project (PRF) - Max Team Size is highly overall correlated with People (PRF) - BA team experience >3 yr and 13 other fieldsHigh correlation
Project (PRF) - Normalised Level 1 PDR (ufp) is highly overall correlated with People (PRF) - IT experience >3 yr and 7 other fieldsHigh correlation
Project (PRF) - Normalised PDR (ufp) is highly overall correlated with People (PRF) - IT experience >3 yr and 7 other fieldsHigh correlation
Project (PRF) - Normalised Work Effort is highly overall correlated with People (PRF) - IT experience >3 yr and 5 other fieldsHigh correlation
Project (PRF) - Normalised Work Effort Level 1 is highly overall correlated with People (PRF) - IT experience 1 to 3 yr and 6 other fieldsHigh correlation
Project (PRF) - Project Elapsed Time is highly overall correlated with Project (PRF) - Manpower Delivery Rate and 2 other fieldsHigh correlation
Project (PRF) - Relative Size is highly overall correlated with Project (PRF) - Functional Size and 1 other fieldsHigh correlation
Project (PRF) - Speed of Delivery is highly overall correlated with Project (PRF) - Functional Size and 4 other fieldsHigh correlation
Project (PRF) - Team Size Group is highly overall correlated with People (PRF) - BA team experience 1 to 3 yr and 4 other fieldsHigh correlation
Project (PRF) - Total project cost is highly overall correlated with People (PRF) - IT experience >3 yr and 4 other fieldsHigh correlation
Project (PRF) - Year of Project is highly overall correlated with People (PRF) - IT experience 1 to 3 yr and 2 other fieldsHigh correlation
Tech (TF) - Architecture is highly overall correlated with Tech (TF) - Client/Server Description and 2 other fieldsHigh correlation
Tech (TF) - Client/Server Description is highly overall correlated with - CASE Tool Used and 8 other fieldsHigh correlation
Tech (TF) - DBMS Used is highly overall correlated with People (PRF) - IT experience 1 to 3 yr and 5 other fieldsHigh correlation
Tech (TF) - Development Platform is highly overall correlated with Tech (TF) - Client/Server Description and 2 other fieldsHigh correlation
Tech (TF) - Language Type is highly overall correlated with Project (PRF) - Cost currencyHigh correlation
Tech (TF) - Server Roles is highly overall correlated with - CASE Tool Used and 16 other fieldsHigh correlation
Tech (TF) - Tools Used is highly overall correlated with Process (PMF) - Docs and 2 other fieldsHigh correlation
Tech (TF) - Type of Server is highly overall correlated with - CASE Tool Used and 14 other fieldsHigh correlation
Tech (TF) - Web Development is highly overall correlated with People (PRF) - BA team experience 1 to 3 yr and 25 other fieldsHigh correlation
Project (PRF) - Application Group is highly imbalanced (76.2%) Imbalance
Tech (TF) - Language Type is highly imbalanced (56.5%) Imbalance
Process (PMF) - Development Methodologies is highly imbalanced (74.1%) Imbalance
Tech (TF) - Web Development is highly imbalanced (97.9%) Imbalance
Tech (TF) - DBMS Used is highly imbalanced (88.0%) Imbalance
People (PRF) - Project manage changes is highly imbalanced (60.2%) Imbalance
Project (PRF) - Cost currency is highly imbalanced (58.6%) Imbalance
Project (PRF) - Currency multiple is highly imbalanced (96.2%) Imbalance
External (EEF) - Industry Sector has 1222 (17.3%) missing values Missing
External (EEF) - Organisation Type has 1205 (17.1%) missing values Missing
Project (PRF) - Application Group has 2096 (29.7%) missing values Missing
Project (PRF) - Application Type has 1467 (20.8%) missing values Missing
Tech (TF) - Development Platform has 1860 (26.4%) missing values Missing
Tech (TF) - Language Type has 1292 (18.3%) missing values Missing
Tech (TF) - Primary Programming Language has 1811 (25.7%) missing values Missing
Project (PRF) - Functional Size has 908 (12.9%) missing values Missing
Project (PRF) - Relative Size has 171 (2.4%) missing values Missing
Project (PRF) - Normalised Work Effort Level 1 has 770 (10.9%) missing values Missing
Project (PRF) - Normalised Level 1 PDR (ufp) has 1648 (23.3%) missing values Missing
Project (PRF) - Normalised PDR (ufp) has 908 (12.9%) missing values Missing
Project (PRF) - Defect Density has 5604 (79.4%) missing values Missing
Project (PRF) - Speed of Delivery has 1619 (22.9%) missing values Missing
Project (PRF) - Manpower Delivery Rate has 5039 (71.4%) missing values Missing
Project (PRF) - Project Elapsed Time has 859 (12.2%) missing values Missing
Project (PRF) - Team Size Group has 4817 (68.2%) missing values Missing
Project (PRF) - Max Team Size has 4817 (68.2%) missing values Missing
- CASE Tool Used has 4887 (69.2%) missing values Missing
Process (PMF) - Development Methodologies has 4488 (63.6%) missing values Missing
Process (PMF) - Prototyping Used has 6043 (85.6%) missing values Missing
Tech (TF) - Architecture has 4323 (61.2%) missing values Missing
Tech (TF) - Client Server? has 6779 (96.0%) missing values Missing
Tech (TF) - Client Roles has 6791 (96.2%) missing values Missing
Tech (TF) - Server Roles has 5693 (80.7%) missing values Missing
Tech (TF) - Type of Server has 6474 (91.7%) missing values Missing
Tech (TF) - Client/Server Description has 6115 (86.6%) missing values Missing
Tech (TF) - Web Development has 6054 (85.8%) missing values Missing
Tech (TF) - DBMS Used has 3735 (52.9%) missing values Missing
Tech (TF) - Tools Used has 2618 (37.1%) missing values Missing
People (PRF) - Project user involvement has 6380 (90.4%) missing values Missing
People (PRF) - BA team experience <1 yr has 6784 (96.1%) missing values Missing
People (PRF) - BA team experience 1 to 3 yr has 6764 (95.8%) missing values Missing
People (PRF) - BA team experience >3 yr has 6697 (94.9%) missing values Missing
People (PRF) - IT experience <1 yr has 7001 (99.2%) missing values Missing
People (PRF) - IT experience 1 to 3 yr has 6990 (99.0%) missing values Missing
People (PRF) - IT experience >3 yr has 6976 (98.8%) missing values Missing
People (PRF) - IT experience <3 yr has 6785 (96.1%) missing values Missing
People (PRF) - IT experience 3 to 9 yr has 6717 (95.2%) missing values Missing
People (PRF) - IT experience >9 yr has 6761 (95.8%) missing values Missing
People (PRF) - Project manage experience has 6633 (94.0%) missing values Missing
People (PRF) - Project manage changes has 6481 (91.8%) missing values Missing
People (PRF) - Personnel changes has 6287 (89.1%) missing values Missing
Project (PRF) - Total project cost has 6035 (85.5%) missing values Missing
Project (PRF) - Cost currency has 5916 (83.8%) missing values Missing
Project (PRF) - Currency multiple has 6255 (88.6%) missing values Missing
Project (PRF) - Defect Density is highly skewed (γ1 = 22.61570279) Skewed
Project (PRF) - Defect Density has 761 (10.8%) zeros Zeros
Project (PRF) - Manpower Delivery Rate has 217 (3.1%) zeros Zeros
Process (PMF) - Docs has 90 (1.3%) zeros Zeros
Tech (TF) - Tools Used has 3063 (43.4%) zeros Zeros
People (PRF) - BA team experience <1 yr has 79 (1.1%) zeros Zeros
People (PRF) - BA team experience 1 to 3 yr has 74 (1.0%) zeros Zeros
People (PRF) - IT experience <3 yr has 73 (1.0%) zeros Zeros
People (PRF) - Personnel changes has 452 (6.4%) zeros Zeros

Reproduction

Analysis started2025-05-15 12:59:43.775590
Analysis finished2025-05-15 13:02:07.361828
Duration2 minutes and 23.59 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ISBSG Project ID
Real number (ℝ)

Distinct7055
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21335.873
Minimum10003
Maximum32767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:07.550836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10003
5-th percentile11135.4
Q115655.5
median21367.5
Q326920.5
95-th percentile31662.3
Maximum32767
Range22764
Interquartile range (IQR)11265

Descriptive statistics

Standard deviation6545.8144
Coefficient of variation (CV)0.30679852
Kurtosis-1.1778567
Mean21335.873
Median Absolute Deviation (MAD)5629
Skewness0.0070489315
Sum1.5058859 × 108
Variance42847686
MonotonicityIncreasing
2025-05-15T14:02:07.835008image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11311 2
 
< 0.1%
31166 2
 
< 0.1%
25713 2
 
< 0.1%
25090 1
 
< 0.1%
25119 1
 
< 0.1%
25117 1
 
< 0.1%
25113 1
 
< 0.1%
25112 1
 
< 0.1%
25110 1
 
< 0.1%
25109 1
 
< 0.1%
Other values (7045) 7045
99.8%
ValueCountFrequency (%)
10003 1
< 0.1%
10011 1
< 0.1%
10012 1
< 0.1%
10014 1
< 0.1%
10015 1
< 0.1%
10019 1
< 0.1%
10026 1
< 0.1%
10028 1
< 0.1%
10029 1
< 0.1%
10033 1
< 0.1%
ValueCountFrequency (%)
32767 1
< 0.1%
32766 1
< 0.1%
32762 1
< 0.1%
32758 1
< 0.1%
32757 1
< 0.1%
32756 1
< 0.1%
32755 1
< 0.1%
32754 1
< 0.1%
32753 1
< 0.1%
32748 1
< 0.1%

External (EEF) - Data Quality Rating
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 KiB
B
5957 
A
1101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7058
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 5957
84.4%
A 1101
 
15.6%

Length

2025-05-15T14:02:08.075130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:08.346267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
b 5957
84.4%
a 1101
 
15.6%

Most occurring characters

ValueCountFrequency (%)
B 5957
84.4%
A 1101
 
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 5957
84.4%
A 1101
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 5957
84.4%
A 1101
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 5957
84.4%
A 1101
 
15.6%

Project (PRF) - Year of Project
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.7638
Minimum1989
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:08.608389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1995
Q12000
median2004
Q32010
95-th percentile2014
Maximum2015
Range26
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8922025
Coefficient of variation (CV)0.0029391006
Kurtosis-0.79299938
Mean2004.7638
Median Absolute Deviation (MAD)4
Skewness0.013083569
Sum14149623
Variance34.71805
MonotonicityNot monotonic
2025-05-15T14:02:08.961056image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2004 698
 
9.9%
2006 606
 
8.6%
2000 554
 
7.8%
1999 515
 
7.3%
2002 418
 
5.9%
2005 416
 
5.9%
2013 397
 
5.6%
2012 364
 
5.2%
2010 329
 
4.7%
2015 306
 
4.3%
Other values (17) 2455
34.8%
ValueCountFrequency (%)
1989 5
 
0.1%
1990 10
 
0.1%
1991 38
 
0.5%
1992 36
 
0.5%
1993 64
 
0.9%
1994 136
1.9%
1995 123
1.7%
1996 94
 
1.3%
1997 149
2.1%
1998 268
3.8%
ValueCountFrequency (%)
2015 306
4.3%
2014 291
4.1%
2013 397
5.6%
2012 364
5.2%
2011 176
 
2.5%
2010 329
4.7%
2009 213
 
3.0%
2008 155
 
2.2%
2007 165
 
2.3%
2006 606
8.6%
Distinct17
Distinct (%)0.3%
Missing1222
Missing (%)17.3%
Memory size55.3 KiB
Communication
1363 
Insurance
1024 
Manufacturing
746 
Government
640 
Medical & Health Care
506 
Other values (12)
1557 

Length

Max length23
Median length21
Mean length12.240233
Min length6

Characters and Unicode

Total characters71434
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCommunication
2nd rowConstruction
3rd rowWholesale & Retail
4th rowWholesale & Retail
5th rowCommunication

Common Values

ValueCountFrequency (%)
Communication 1363
19.3%
Insurance 1024
14.5%
Manufacturing 746
10.6%
Government 640
9.1%
Medical & Health Care 506
 
7.2%
Banking 503
 
7.1%
Financial 377
 
5.3%
Service Industry 174
 
2.5%
Wholesale & Retail 129
 
1.8%
Electronics & Computers 118
 
1.7%
Other values (7) 256
 
3.6%
(Missing) 1222
17.3%

Length

2025-05-15T14:02:09.277784image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
communication 1363
16.9%
insurance 1024
12.7%
753
9.3%
manufacturing 746
9.2%
government 640
7.9%
medical 506
 
6.3%
health 506
 
6.3%
care 506
 
6.3%
banking 503
 
6.2%
financial 377
 
4.7%
Other values (14) 1156
14.3%

Most occurring characters

ValueCountFrequency (%)
n 9809
13.7%
a 6992
 
9.8%
i 6137
 
8.6%
e 5084
 
7.1%
c 4600
 
6.4%
u 4234
 
5.9%
t 4066
 
5.7%
o 3987
 
5.6%
r 3657
 
5.1%
m 3484
 
4.9%
Other values (27) 19384
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 9809
13.7%
a 6992
 
9.8%
i 6137
 
8.6%
e 5084
 
7.1%
c 4600
 
6.4%
u 4234
 
5.9%
t 4066
 
5.7%
o 3987
 
5.6%
r 3657
 
5.1%
m 3484
 
4.9%
Other values (27) 19384
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 9809
13.7%
a 6992
 
9.8%
i 6137
 
8.6%
e 5084
 
7.1%
c 4600
 
6.4%
u 4234
 
5.9%
t 4066
 
5.7%
o 3987
 
5.6%
r 3657
 
5.1%
m 3484
 
4.9%
Other values (27) 19384
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 9809
13.7%
a 6992
 
9.8%
i 6137
 
8.6%
e 5084
 
7.1%
c 4600
 
6.4%
u 4234
 
5.9%
t 4066
 
5.7%
o 3987
 
5.6%
r 3657
 
5.1%
m 3484
 
4.9%
Other values (27) 19384
27.1%
Distinct179
Distinct (%)3.1%
Missing1205
Missing (%)17.1%
Memory size55.3 KiB
2025-05-15T14:02:09.615648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length255
Median length125
Mean length18.168973
Min length4

Characters and Unicode

Total characters106343
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)1.4%

Sample

1st rowTelecommunications;
2nd rowConstruction;
3rd rowBilling;
4th rowWholesale & Retail Trade;
5th rowTelecommunications;
ValueCountFrequency (%)
insurance 1029
 
9.7%
703
 
6.7%
communications 687
 
6.5%
manufacturing 679
 
6.4%
and 537
 
5.1%
health 533
 
5.0%
telecommunications 511
 
4.8%
medical 505
 
4.8%
care 503
 
4.8%
services 490
 
4.6%
Other values (261) 4383
41.5%
2025-05-15T14:02:10.310783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 11468
 
10.8%
e 8647
 
8.1%
a 8540
 
8.0%
i 8300
 
7.8%
; 6416
 
6.0%
c 6089
 
5.7%
r 5501
 
5.2%
t 5344
 
5.0%
o 5089
 
4.8%
s 5065
 
4.8%
Other values (44) 35884
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106343
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 11468
 
10.8%
e 8647
 
8.1%
a 8540
 
8.0%
i 8300
 
7.8%
; 6416
 
6.0%
c 6089
 
5.7%
r 5501
 
5.2%
t 5344
 
5.0%
o 5089
 
4.8%
s 5065
 
4.8%
Other values (44) 35884
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106343
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 11468
 
10.8%
e 8647
 
8.1%
a 8540
 
8.0%
i 8300
 
7.8%
; 6416
 
6.0%
c 6089
 
5.7%
r 5501
 
5.2%
t 5344
 
5.0%
o 5089
 
4.8%
s 5065
 
4.8%
Other values (44) 35884
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106343
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 11468
 
10.8%
e 8647
 
8.1%
a 8540
 
8.0%
i 8300
 
7.8%
; 6416
 
6.0%
c 6089
 
5.7%
r 5501
 
5.2%
t 5344
 
5.0%
o 5089
 
4.8%
s 5065
 
4.8%
Other values (44) 35884
33.7%

Project (PRF) - Application Group
Categorical

Imbalance  Missing 

Distinct6
Distinct (%)0.1%
Missing2096
Missing (%)29.7%
Memory size55.3 KiB
Business Application
4468 
Real-Time Application
 
222
Mathematically-Intensive Application
 
216
Infrastructure Software
 
48
Mathematically intensive application
 
6

Length

Max length46
Median length20
Mean length20.800081
Min length20

Characters and Unicode

Total characters103210
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBusiness Application
2nd rowBusiness Application
3rd rowBusiness Application
4th rowBusiness Application
5th rowBusiness Application

Common Values

ValueCountFrequency (%)
Business Application 4468
63.3%
Real-Time Application 222
 
3.1%
Mathematically-Intensive Application 216
 
3.1%
Infrastructure Software 48
 
0.7%
Mathematically intensive application 6
 
0.1%
Business Application; Infrastructure Software; 2
 
< 0.1%
(Missing) 2096
29.7%

Length

2025-05-15T14:02:10.614155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:10.861783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
application 4914
49.5%
business 4470
45.0%
real-time 222
 
2.2%
mathematically-intensive 216
 
2.2%
infrastructure 50
 
0.5%
software 50
 
0.5%
mathematically 6
 
0.1%
intensive 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 14970
14.5%
s 13682
13.3%
n 9878
9.6%
p 9828
9.5%
a 5908
 
5.7%
t 5730
 
5.6%
e 5680
 
5.5%
l 5580
 
5.4%
c 5186
 
5.0%
4972
 
4.8%
Other values (18) 21796
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 14970
14.5%
s 13682
13.3%
n 9878
9.6%
p 9828
9.5%
a 5908
 
5.7%
t 5730
 
5.6%
e 5680
 
5.5%
l 5580
 
5.4%
c 5186
 
5.0%
4972
 
4.8%
Other values (18) 21796
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 14970
14.5%
s 13682
13.3%
n 9878
9.6%
p 9828
9.5%
a 5908
 
5.7%
t 5730
 
5.6%
e 5680
 
5.5%
l 5580
 
5.4%
c 5186
 
5.0%
4972
 
4.8%
Other values (18) 21796
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 14970
14.5%
s 13682
13.3%
n 9878
9.6%
p 9828
9.5%
a 5908
 
5.7%
t 5730
 
5.6%
e 5680
 
5.5%
l 5580
 
5.4%
c 5186
 
5.0%
4972
 
4.8%
Other values (18) 21796
21.1%
Distinct539
Distinct (%)9.6%
Missing1467
Missing (%)20.8%
Memory size55.3 KiB
2025-05-15T14:02:11.253832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length255
Median length211
Mean length30.470935
Min length3

Characters and Unicode

Total characters170363
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)5.7%

Sample

1st rowOnline. eSales;
2nd rowStock control & order processing;
3rd rowBilling;
4th rowManagement Information System;
5th rowData Warehouse system;
ValueCountFrequency (%)
financial 1300
 
7.9%
management 1259
 
7.6%
transaction 1191
 
7.2%
system 1079
 
6.5%
process/accounting 974
 
5.9%
application 563
 
3.4%
information 482
 
2.9%
not 475
 
2.9%
recorded 475
 
2.9%
transaction/production 455
 
2.8%
Other values (583) 8275
50.1%
2025-05-15T14:02:11.954286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 18522
 
10.9%
a 14907
 
8.8%
t 12769
 
7.5%
e 12491
 
7.3%
o 12340
 
7.2%
i 11667
 
6.8%
10943
 
6.4%
c 10326
 
6.1%
r 9293
 
5.5%
s 9085
 
5.3%
Other values (53) 48020
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 170363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 18522
 
10.9%
a 14907
 
8.8%
t 12769
 
7.5%
e 12491
 
7.3%
o 12340
 
7.2%
i 11667
 
6.8%
10943
 
6.4%
c 10326
 
6.1%
r 9293
 
5.5%
s 9085
 
5.3%
Other values (53) 48020
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 170363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 18522
 
10.9%
a 14907
 
8.8%
t 12769
 
7.5%
e 12491
 
7.3%
o 12340
 
7.2%
i 11667
 
6.8%
10943
 
6.4%
c 10326
 
6.1%
r 9293
 
5.5%
s 9085
 
5.3%
Other values (53) 48020
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 170363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 18522
 
10.9%
a 14907
 
8.8%
t 12769
 
7.5%
e 12491
 
7.3%
o 12340
 
7.2%
i 11667
 
6.8%
10943
 
6.4%
c 10326
 
6.1%
r 9293
 
5.5%
s 9085
 
5.3%
Other values (53) 48020
28.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 KiB
Enhancement
4834 
New Development
2129 
Re-development
 
95

Length

Max length15
Median length11
Mean length12.246954
Min length11

Characters and Unicode

Total characters86439
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnhancement
2nd rowNew Development
3rd rowEnhancement
4th rowEnhancement
5th rowEnhancement

Common Values

ValueCountFrequency (%)
Enhancement 4834
68.5%
New Development 2129
30.2%
Re-development 95
 
1.3%

Length

2025-05-15T14:02:12.187485image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:12.349653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
enhancement 4834
52.6%
new 2129
23.2%
development 2129
23.2%
re-development 95
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 18564
21.5%
n 16726
19.4%
m 7058
 
8.2%
t 7058
 
8.2%
E 4834
 
5.6%
h 4834
 
5.6%
a 4834
 
5.6%
c 4834
 
5.6%
v 2224
 
2.6%
p 2224
 
2.6%
Other values (9) 13249
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18564
21.5%
n 16726
19.4%
m 7058
 
8.2%
t 7058
 
8.2%
E 4834
 
5.6%
h 4834
 
5.6%
a 4834
 
5.6%
c 4834
 
5.6%
v 2224
 
2.6%
p 2224
 
2.6%
Other values (9) 13249
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18564
21.5%
n 16726
19.4%
m 7058
 
8.2%
t 7058
 
8.2%
E 4834
 
5.6%
h 4834
 
5.6%
a 4834
 
5.6%
c 4834
 
5.6%
v 2224
 
2.6%
p 2224
 
2.6%
Other values (9) 13249
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18564
21.5%
n 16726
19.4%
m 7058
 
8.2%
t 7058
 
8.2%
E 4834
 
5.6%
h 4834
 
5.6%
a 4834
 
5.6%
c 4834
 
5.6%
v 2224
 
2.6%
p 2224
 
2.6%
Other values (9) 13249
15.3%

Tech (TF) - Development Platform
Categorical

High correlation  Missing 

Distinct6
Distinct (%)0.1%
Missing1860
Missing (%)26.4%
Memory size55.3 KiB
Multi
1942 
MF
1703 
PC
981 
MR
559 
Proprietary
 
12

Length

Max length11
Median length2
Mean length3.1429396
Min length2

Characters and Unicode

Total characters16337
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMulti
2nd rowMulti
3rd rowMF
4th rowMulti
5th rowMF

Common Values

ValueCountFrequency (%)
Multi 1942
27.5%
MF 1703
24.1%
PC 981
13.9%
MR 559
 
7.9%
Proprietary 12
 
0.2%
Hand Held 1
 
< 0.1%
(Missing) 1860
26.4%

Length

2025-05-15T14:02:12.555715image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:12.757120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
multi 1942
37.4%
mf 1703
32.8%
pc 981
18.9%
mr 559
 
10.8%
proprietary 12
 
0.2%
hand 1
 
< 0.1%
held 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 4204
25.7%
t 1954
12.0%
i 1954
12.0%
l 1943
11.9%
u 1942
11.9%
F 1703
10.4%
P 993
 
6.1%
C 981
 
6.0%
R 559
 
3.4%
r 36
 
0.2%
Other values (9) 68
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 4204
25.7%
t 1954
12.0%
i 1954
12.0%
l 1943
11.9%
u 1942
11.9%
F 1703
10.4%
P 993
 
6.1%
C 981
 
6.0%
R 559
 
3.4%
r 36
 
0.2%
Other values (9) 68
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 4204
25.7%
t 1954
12.0%
i 1954
12.0%
l 1943
11.9%
u 1942
11.9%
F 1703
10.4%
P 993
 
6.1%
C 981
 
6.0%
R 559
 
3.4%
r 36
 
0.2%
Other values (9) 68
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 4204
25.7%
t 1954
12.0%
i 1954
12.0%
l 1943
11.9%
u 1942
11.9%
F 1703
10.4%
P 993
 
6.1%
C 981
 
6.0%
R 559
 
3.4%
r 36
 
0.2%
Other values (9) 68
 
0.4%

Tech (TF) - Language Type
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)0.1%
Missing1292
Missing (%)18.3%
Memory size55.3 KiB
3GL
3716 
4GL
1863 
ApG
 
155
2GL
 
18
5GL
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17298
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4GL
2nd row3GL
3rd row3GL
4th row3GL
5th row3GL

Common Values

ValueCountFrequency (%)
3GL 3716
52.6%
4GL 1863
26.4%
ApG 155
 
2.2%
2GL 18
 
0.3%
5GL 13
 
0.2%
APG 1
 
< 0.1%
(Missing) 1292
 
18.3%

Length

2025-05-15T14:02:13.017123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:13.243947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3gl 3716
64.4%
4gl 1863
32.3%
apg 156
 
2.7%
2gl 18
 
0.3%
5gl 13
 
0.2%

Most occurring characters

ValueCountFrequency (%)
G 5766
33.3%
L 5610
32.4%
3 3716
21.5%
4 1863
 
10.8%
A 156
 
0.9%
p 155
 
0.9%
2 18
 
0.1%
5 13
 
0.1%
P 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 5766
33.3%
L 5610
32.4%
3 3716
21.5%
4 1863
 
10.8%
A 156
 
0.9%
p 155
 
0.9%
2 18
 
0.1%
5 13
 
0.1%
P 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 5766
33.3%
L 5610
32.4%
3 3716
21.5%
4 1863
 
10.8%
A 156
 
0.9%
p 155
 
0.9%
2 18
 
0.1%
5 13
 
0.1%
P 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 5766
33.3%
L 5610
32.4%
3 3716
21.5%
4 1863
 
10.8%
A 156
 
0.9%
p 155
 
0.9%
2 18
 
0.1%
5 13
 
0.1%
P 1
 
< 0.1%
Distinct123
Distinct (%)2.3%
Missing1811
Missing (%)25.7%
Memory size55.3 KiB
2025-05-15T14:02:13.520211image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length35
Median length32
Mean length5.3421002
Min length1

Characters and Unicode

Total characters28030
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.7%

Sample

1st rowAccess
2nd rowCOBOL
3rd rowCOBOL
4th rowShell
5th rowJava
ValueCountFrequency (%)
java 1081
18.5%
cobol 918
15.7%
c 671
11.5%
visual 463
 
7.9%
basic 432
 
7.4%
pl/i 360
 
6.2%
oracle 239
 
4.1%
net 194
 
3.3%
sql 139
 
2.4%
abap 103
 
1.8%
Other values (128) 1230
21.1%
2025-05-15T14:02:14.179372image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3475
 
12.4%
O 2398
 
8.6%
L 1996
 
7.1%
C 1817
 
6.5%
B 1580
 
5.6%
s 1118
 
4.0%
v 1100
 
3.9%
J 1099
 
3.9%
i 1090
 
3.9%
l 1057
 
3.8%
Other values (52) 11300
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3475
 
12.4%
O 2398
 
8.6%
L 1996
 
7.1%
C 1817
 
6.5%
B 1580
 
5.6%
s 1118
 
4.0%
v 1100
 
3.9%
J 1099
 
3.9%
i 1090
 
3.9%
l 1057
 
3.8%
Other values (52) 11300
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3475
 
12.4%
O 2398
 
8.6%
L 1996
 
7.1%
C 1817
 
6.5%
B 1580
 
5.6%
s 1118
 
4.0%
v 1100
 
3.9%
J 1099
 
3.9%
i 1090
 
3.9%
l 1057
 
3.8%
Other values (52) 11300
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3475
 
12.4%
O 2398
 
8.6%
L 1996
 
7.1%
C 1817
 
6.5%
B 1580
 
5.6%
s 1118
 
4.0%
v 1100
 
3.9%
J 1099
 
3.9%
i 1090
 
3.9%
l 1057
 
3.8%
Other values (52) 11300
40.3%

Project (PRF) - Functional Size
Real number (ℝ)

High correlation  Missing 

Distinct1160
Distinct (%)18.9%
Missing908
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean316.9335
Minimum2
Maximum16148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:14.370335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13
Q164
median137
Q3323
95-th percentile1204.4
Maximum16148
Range16146
Interquartile range (IQR)259

Descriptive statistics

Standard deviation640.30248
Coefficient of variation (CV)2.0203055
Kurtosis165.79925
Mean316.9335
Median Absolute Deviation (MAD)93
Skewness9.7347084
Sum1949141
Variance409987.26
MonotonicityNot monotonic
2025-05-15T14:02:14.738849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 45
 
0.6%
51 44
 
0.6%
72 41
 
0.6%
12 41
 
0.6%
14 38
 
0.5%
54 38
 
0.5%
6 37
 
0.5%
4 37
 
0.5%
53 37
 
0.5%
58 36
 
0.5%
Other values (1150) 5756
81.6%
(Missing) 908
 
12.9%
ValueCountFrequency (%)
2 4
 
0.1%
3 24
0.3%
4 37
0.5%
5 26
0.4%
6 37
0.5%
7 45
0.6%
8 22
0.3%
9 22
0.3%
10 26
0.4%
11 15
 
0.2%
ValueCountFrequency (%)
16148 1
< 0.1%
14656 1
< 0.1%
13580 1
< 0.1%
10024 1
< 0.1%
9390 1
< 0.1%
7599 1
< 0.1%
7474 1
< 0.1%
7400 1
< 0.1%
7134 1
< 0.1%
6340 1
< 0.1%

Project (PRF) - Relative Size
Categorical

High correlation  Missing 

Distinct9
Distinct (%)0.1%
Missing171
Missing (%)2.4%
Memory size55.3 KiB
M1
2327 
S
1928 
M2
1387 
XS
543 
L
391 
Other values (4)
311 

Length

Max length4
Median length2
Mean length1.7020473
Min length1

Characters and Unicode

Total characters11722
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS
2nd rowM2
3rd rowS
4th rowXXS
5th rowM2

Common Values

ValueCountFrequency (%)
M1 2327
33.0%
S 1928
27.3%
M2 1387
19.7%
XS 543
 
7.7%
L 391
 
5.5%
XXS 255
 
3.6%
XL 45
 
0.6%
XXL 10
 
0.1%
XXXL 1
 
< 0.1%
(Missing) 171
 
2.4%

Length

2025-05-15T14:02:15.054412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:15.348171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
m1 2327
33.8%
s 1928
28.0%
m2 1387
20.1%
xs 543
 
7.9%
l 391
 
5.7%
xxs 255
 
3.7%
xl 45
 
0.7%
xxl 10
 
0.1%
xxxl 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 3714
31.7%
S 2726
23.3%
1 2327
19.9%
2 1387
 
11.8%
X 1121
 
9.6%
L 447
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 3714
31.7%
S 2726
23.3%
1 2327
19.9%
2 1387
 
11.8%
X 1121
 
9.6%
L 447
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 3714
31.7%
S 2726
23.3%
1 2327
19.9%
2 1387
 
11.8%
X 1121
 
9.6%
L 447
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 3714
31.7%
S 2726
23.3%
1 2327
19.9%
2 1387
 
11.8%
X 1121
 
9.6%
L 447
 
3.8%

Project (PRF) - Normalised Work Effort Level 1
Real number (ℝ)

High correlation  Missing 

Distinct3792
Distinct (%)60.3%
Missing770
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean3888.2362
Minimum4
Maximum230514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:15.576837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile130
Q1619
median1550.5
Q33847.25
95-th percentile14208.9
Maximum230514
Range230510
Interquartile range (IQR)3228.25

Descriptive statistics

Standard deviation8674.1671
Coefficient of variation (CV)2.2308745
Kurtosis167.29897
Mean3888.2362
Median Absolute Deviation (MAD)1175.5
Skewness9.9098103
Sum24449229
Variance75241174
MonotonicityNot monotonic
2025-05-15T14:02:15.815228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 9
 
0.1%
62 9
 
0.1%
500 9
 
0.1%
473 9
 
0.1%
620 9
 
0.1%
995 8
 
0.1%
300 8
 
0.1%
741 8
 
0.1%
304 8
 
0.1%
162 8
 
0.1%
Other values (3782) 6203
87.9%
(Missing) 770
 
10.9%
ValueCountFrequency (%)
4 1
 
< 0.1%
6 1
 
< 0.1%
8 2
< 0.1%
9 3
< 0.1%
11 4
0.1%
12 2
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 2
< 0.1%
16 3
< 0.1%
ValueCountFrequency (%)
230514 1
< 0.1%
202386 1
< 0.1%
141374 1
< 0.1%
134211 1
< 0.1%
126133 1
< 0.1%
115816 1
< 0.1%
106480 1
< 0.1%
104248 1
< 0.1%
92380 1
< 0.1%
89143 1
< 0.1%

Project (PRF) - Normalised Work Effort
Real number (ℝ)

High correlation 

Distinct4214
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4314.5584
Minimum4
Maximum266946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:16.080792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile144
Q1687
median1666
Q34106.75
95-th percentile15822.15
Maximum266946
Range266942
Interquartile range (IQR)3419.75

Descriptive statistics

Standard deviation10076.658
Coefficient of variation (CV)2.3355016
Kurtosis166.11369
Mean4314.5584
Median Absolute Deviation (MAD)1245
Skewness10.012215
Sum30452153
Variance1.0153904 × 108
MonotonicityNot monotonic
2025-05-15T14:02:16.353114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
995 9
 
0.1%
304 9
 
0.1%
620 9
 
0.1%
473 9
 
0.1%
62 9
 
0.1%
300 9
 
0.1%
40 8
 
0.1%
500 8
 
0.1%
840 8
 
0.1%
420 8
 
0.1%
Other values (4204) 6972
98.8%
ValueCountFrequency (%)
4 1
 
< 0.1%
6 1
 
< 0.1%
8 2
< 0.1%
9 4
0.1%
11 4
0.1%
12 2
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 2
< 0.1%
16 3
< 0.1%
ValueCountFrequency (%)
266946 1
< 0.1%
230514 1
< 0.1%
202386 1
< 0.1%
164764 1
< 0.1%
150040 1
< 0.1%
134211 1
< 0.1%
126133 1
< 0.1%
124650 1
< 0.1%
115816 1
< 0.1%
110624 1
< 0.1%

Project (PRF) - Normalised Level 1 PDR (ufp)
Real number (ℝ)

High correlation  Missing 

Distinct988
Distinct (%)18.3%
Missing1648
Missing (%)23.3%
Infinite0
Infinite (%)0.0%
Mean30.648318
Minimum0.1
Maximum2816.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:16.600335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.8
Q15.4
median11.1
Q323.8
95-th percentile121.41
Maximum2816.3
Range2816.2
Interquartile range (IQR)18.4

Descriptive statistics

Standard deviation85.292971
Coefficient of variation (CV)2.7829577
Kurtosis292.18966
Mean30.648318
Median Absolute Deviation (MAD)7
Skewness12.97931
Sum165807.4
Variance7274.8909
MonotonicityNot monotonic
2025-05-15T14:02:16.817610image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 41
 
0.6%
3.6 39
 
0.6%
4.8 37
 
0.5%
5.6 36
 
0.5%
3 36
 
0.5%
5 36
 
0.5%
5.1 35
 
0.5%
6.3 35
 
0.5%
4.2 35
 
0.5%
3.9 35
 
0.5%
Other values (978) 5045
71.5%
(Missing) 1648
 
23.3%
ValueCountFrequency (%)
0.1 2
 
< 0.1%
0.2 4
 
0.1%
0.3 10
0.1%
0.4 6
 
0.1%
0.5 9
0.1%
0.6 9
0.1%
0.7 14
0.2%
0.8 16
0.2%
0.9 14
0.2%
1 17
0.2%
ValueCountFrequency (%)
2816.3 1
< 0.1%
1799.7 1
< 0.1%
1374.6 1
< 0.1%
1264.8 1
< 0.1%
1113.3 1
< 0.1%
1097.4 1
< 0.1%
1039.9 1
< 0.1%
1009.9 1
< 0.1%
958 1
< 0.1%
922.6 1
< 0.1%

Project (PRF) - Normalised PDR (ufp)
Real number (ℝ)

High correlation  Missing 

Distinct1019
Distinct (%)16.6%
Missing908
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean29.188016
Minimum0.1
Maximum2816.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:17.013193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.9
Q15.7
median11.6
Q323.475
95-th percentile106.74
Maximum2816.3
Range2816.2
Interquartile range (IQR)17.775

Descriptive statistics

Standard deviation80.295994
Coefficient of variation (CV)2.7509918
Kurtosis328.5503
Mean29.188016
Median Absolute Deviation (MAD)7.1
Skewness13.739469
Sum179506.3
Variance6447.4467
MonotonicityNot monotonic
2025-05-15T14:02:17.261687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 45
 
0.6%
4.4 43
 
0.6%
4.8 41
 
0.6%
6.3 40
 
0.6%
6.5 40
 
0.6%
3 39
 
0.6%
3.6 39
 
0.6%
9.3 38
 
0.5%
7.3 38
 
0.5%
4.5 38
 
0.5%
Other values (1009) 5749
81.5%
(Missing) 908
 
12.9%
ValueCountFrequency (%)
0.1 3
 
< 0.1%
0.2 1
 
< 0.1%
0.3 7
 
0.1%
0.4 5
 
0.1%
0.5 8
0.1%
0.6 6
 
0.1%
0.7 13
0.2%
0.8 18
0.3%
0.9 16
0.2%
1 19
0.3%
ValueCountFrequency (%)
2816.3 1
< 0.1%
1799.7 1
< 0.1%
1374.6 1
< 0.1%
1264.8 1
< 0.1%
1113.3 1
< 0.1%
1097.4 1
< 0.1%
1039.9 1
< 0.1%
1009.9 1
< 0.1%
958 1
< 0.1%
922.6 1
< 0.1%

Project (PRF) - Defect Density
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct395
Distinct (%)27.2%
Missing5604
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean24.286657
Minimum0
Maximum4236.8
Zeros761
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:17.488928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317.2
95-th percentile80.525
Maximum4236.8
Range4236.8
Interquartile range (IQR)17.2

Descriptive statistics

Standard deviation137.52385
Coefficient of variation (CV)5.662527
Kurtosis637.45574
Mean24.286657
Median Absolute Deviation (MAD)0
Skewness22.615703
Sum35312.8
Variance18912.811
MonotonicityNot monotonic
2025-05-15T14:02:17.737026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 761
 
10.8%
2.1 7
 
0.1%
3.7 6
 
0.1%
3.6 6
 
0.1%
11.1 6
 
0.1%
5.8 6
 
0.1%
22.7 6
 
0.1%
16.5 5
 
0.1%
55.6 5
 
0.1%
8 5
 
0.1%
Other values (385) 641
 
9.1%
(Missing) 5604
79.4%
ValueCountFrequency (%)
0 761
10.8%
0.6 2
 
< 0.1%
0.8 1
 
< 0.1%
0.9 3
 
< 0.1%
1 1
 
< 0.1%
1.2 2
 
< 0.1%
1.3 3
 
< 0.1%
1.4 2
 
< 0.1%
1.5 1
 
< 0.1%
1.6 2
 
< 0.1%
ValueCountFrequency (%)
4236.8 1
< 0.1%
1936 1
< 0.1%
1120.7 1
< 0.1%
1111.1 1
< 0.1%
593.6 1
< 0.1%
558.1 1
< 0.1%
465.5 1
< 0.1%
411.3 1
< 0.1%
397.4 1
< 0.1%
388.9 1
< 0.1%

Project (PRF) - Speed of Delivery
Real number (ℝ)

High correlation  Missing 

Distinct1414
Distinct (%)26.0%
Missing1619
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean49.783747
Minimum0.2
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:17.984504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.5
Q112
median26.6
Q357
95-th percentile163.82
Maximum2000
Range1999.8
Interquartile range (IQR)45

Descriptive statistics

Standard deviation81.913083
Coefficient of variation (CV)1.645378
Kurtosis115.81085
Mean49.783747
Median Absolute Deviation (MAD)18.2
Skewness7.7767388
Sum270773.8
Variance6709.7531
MonotonicityNot monotonic
2025-05-15T14:02:18.310678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 27
 
0.4%
1.1 26
 
0.4%
1 24
 
0.3%
0.8 23
 
0.3%
0.9 22
 
0.3%
10 22
 
0.3%
0.6 22
 
0.3%
2.3 21
 
0.3%
5 21
 
0.3%
1.2 20
 
0.3%
Other values (1404) 5211
73.8%
(Missing) 1619
 
22.9%
ValueCountFrequency (%)
0.2 9
 
0.1%
0.3 11
0.2%
0.4 13
0.2%
0.5 19
0.3%
0.6 22
0.3%
0.7 19
0.3%
0.8 23
0.3%
0.9 22
0.3%
1 24
0.3%
1.1 26
0.4%
ValueCountFrequency (%)
2000 1
< 0.1%
1550 1
< 0.1%
1480.4 1
< 0.1%
970 1
< 0.1%
949.9 1
< 0.1%
897.3 1
< 0.1%
724.4 1
< 0.1%
707.5 1
< 0.1%
681.7 1
< 0.1%
672.4 1
< 0.1%

Project (PRF) - Manpower Delivery Rate
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct392
Distinct (%)19.4%
Missing5039
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean10.03264
Minimum0
Maximum293.5
Zeros217
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:18.647622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median4
Q310.5
95-th percentile39.91
Maximum293.5
Range293.5
Interquartile range (IQR)9.9

Descriptive statistics

Standard deviation19.856483
Coefficient of variation (CV)1.9791882
Kurtosis51.365931
Mean10.03264
Median Absolute Deviation (MAD)3.9
Skewness5.7641177
Sum20255.9
Variance394.27991
MonotonicityNot monotonic
2025-05-15T14:02:18.975017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 217
 
3.1%
0.1 157
 
2.2%
0.2 55
 
0.8%
0.3 31
 
0.4%
1.3 30
 
0.4%
4 24
 
0.3%
2 23
 
0.3%
2.5 21
 
0.3%
0.9 21
 
0.3%
0.8 21
 
0.3%
Other values (382) 1419
 
20.1%
(Missing) 5039
71.4%
ValueCountFrequency (%)
0 217
3.1%
0.1 157
2.2%
0.2 55
 
0.8%
0.3 31
 
0.4%
0.4 19
 
0.3%
0.5 18
 
0.3%
0.6 10
 
0.1%
0.7 15
 
0.2%
0.8 21
 
0.3%
0.9 21
 
0.3%
ValueCountFrequency (%)
293.5 1
< 0.1%
258.3 1
< 0.1%
200 1
< 0.1%
194.7 1
< 0.1%
186 1
< 0.1%
151.7 1
< 0.1%
148 1
< 0.1%
139.5 1
< 0.1%
138.4 1
< 0.1%
137.8 1
< 0.1%

Project (PRF) - Project Elapsed Time
Real number (ℝ)

High correlation  Missing 

Distinct308
Distinct (%)5.0%
Missing859
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean7.7801387
Minimum0.03
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:19.243388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile1.2
Q13.2
median6
Q310
95-th percentile21
Maximum87
Range86.97
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation6.9621346
Coefficient of variation (CV)0.89485996
Kurtosis19.731714
Mean7.7801387
Median Absolute Deviation (MAD)3
Skewness3.1535169
Sum48229.08
Variance48.471318
MonotonicityNot monotonic
2025-05-15T14:02:19.518800image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 382
 
5.4%
6 336
 
4.8%
5 297
 
4.2%
4 297
 
4.2%
8 226
 
3.2%
7 223
 
3.2%
2 208
 
2.9%
12 207
 
2.9%
9 194
 
2.7%
10 186
 
2.6%
Other values (298) 3643
51.6%
(Missing) 859
 
12.2%
ValueCountFrequency (%)
0.03 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.12 1
 
< 0.1%
0.15 1
 
< 0.1%
0.16 1
 
< 0.1%
0.18 1
 
< 0.1%
0.2 8
0.1%
0.25 5
0.1%
0.26 1
 
< 0.1%
ValueCountFrequency (%)
87 2
< 0.1%
85.2 1
< 0.1%
84 1
< 0.1%
81 1
< 0.1%
78 1
< 0.1%
65 1
< 0.1%
56.9 1
< 0.1%
55 1
< 0.1%
54.3 1
< 0.1%
54 1
< 0.1%

Project (PRF) - Team Size Group
Categorical

High correlation  Missing 

Distinct15
Distinct (%)0.7%
Missing4817
Missing (%)68.2%
Memory size55.3 KiB
5-8
585 
3-4
356 
9-14
335 
2
175 
15-20
156 
Other values (10)
634 

Length

Max length6
Median length5
Mean length3.4203481
Min length1

Characters and Unicode

Total characters7665
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row101+
2nd row81-90
3rd row81-90
4th row5-8
5th row5-8

Common Values

ValueCountFrequency (%)
5-8 585
 
8.3%
3-4 356
 
5.0%
9-14 335
 
4.7%
2 175
 
2.5%
15-20 156
 
2.2%
21-30 149
 
2.1%
1 146
 
2.1%
31-40 94
 
1.3%
41-50 56
 
0.8%
101+ 53
 
0.8%
Other values (5) 136
 
1.9%
(Missing) 4817
68.2%

Length

2025-05-15T14:02:19.721533image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5-8 585
26.1%
3-4 356
15.9%
9-14 335
14.9%
2 175
 
7.8%
15-20 156
 
7.0%
21-30 149
 
6.6%
1 146
 
6.5%
31-40 94
 
4.2%
41-50 56
 
2.5%
101 53
 
2.4%
Other values (5) 136
 
6.1%

Most occurring characters

ValueCountFrequency (%)
- 1867
24.4%
1 1192
15.6%
4 841
11.0%
5 835
10.9%
0 658
 
8.6%
8 636
 
8.3%
3 599
 
7.8%
2 480
 
6.3%
9 376
 
4.9%
6 71
 
0.9%
Other values (2) 110
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1867
24.4%
1 1192
15.6%
4 841
11.0%
5 835
10.9%
0 658
 
8.6%
8 636
 
8.3%
3 599
 
7.8%
2 480
 
6.3%
9 376
 
4.9%
6 71
 
0.9%
Other values (2) 110
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1867
24.4%
1 1192
15.6%
4 841
11.0%
5 835
10.9%
0 658
 
8.6%
8 636
 
8.3%
3 599
 
7.8%
2 480
 
6.3%
9 376
 
4.9%
6 71
 
0.9%
Other values (2) 110
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1867
24.4%
1 1192
15.6%
4 841
11.0%
5 835
10.9%
0 658
 
8.6%
8 636
 
8.3%
3 599
 
7.8%
2 480
 
6.3%
9 376
 
4.9%
6 71
 
0.9%
Other values (2) 110
 
1.4%

Project (PRF) - Max Team Size
Real number (ℝ)

High correlation  Missing 

Distinct161
Distinct (%)7.2%
Missing4817
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean17.103235
Minimum0.3
Maximum309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:19.971580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1
Q14
median7
Q317
95-th percentile72
Maximum309
Range308.7
Interquartile range (IQR)13

Descriptive statistics

Standard deviation26.679857
Coefficient of variation (CV)1.5599304
Kurtosis18.542057
Mean17.103235
Median Absolute Deviation (MAD)4
Skewness3.6290395
Sum38328.35
Variance711.81475
MonotonicityNot monotonic
2025-05-15T14:02:20.177870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 193
 
2.7%
4 183
 
2.6%
2 171
 
2.4%
3 162
 
2.3%
6 143
 
2.0%
1 140
 
2.0%
7 133
 
1.9%
8 106
 
1.5%
10 81
 
1.1%
9 79
 
1.1%
Other values (151) 850
 
12.0%
(Missing) 4817
68.2%
ValueCountFrequency (%)
0.3 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
0.95 1
 
< 0.1%
1 140
2.0%
1.5 2
 
< 0.1%
1.65 1
 
< 0.1%
1.75 1
 
< 0.1%
2 171
2.4%
2.1 1
 
< 0.1%
ValueCountFrequency (%)
309 1
< 0.1%
253 1
< 0.1%
205 1
< 0.1%
193 1
< 0.1%
188 1
< 0.1%
186 1
< 0.1%
176 1
< 0.1%
172 1
< 0.1%
163 1
< 0.1%
156 1
< 0.1%

- CASE Tool Used
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing4887
Missing (%)69.2%
Memory size55.3 KiB
No
1021 
Yes
836 
Don't Know
314 

Length

Max length10
Median length3
Mean length3.5421465
Min length2

Characters and Unicode

Total characters7690
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1021
 
14.5%
Yes 836
 
11.8%
Don't Know 314
 
4.4%
(Missing) 4887
69.2%

Length

2025-05-15T14:02:20.383295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:20.572839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
no 1021
41.1%
yes 836
33.6%
don't 314
 
12.6%
know 314
 
12.6%

Most occurring characters

ValueCountFrequency (%)
o 1649
21.4%
N 1021
13.3%
Y 836
10.9%
e 836
10.9%
s 836
10.9%
n 628
 
8.2%
D 314
 
4.1%
' 314
 
4.1%
t 314
 
4.1%
314
 
4.1%
Other values (2) 628
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1649
21.4%
N 1021
13.3%
Y 836
10.9%
e 836
10.9%
s 836
10.9%
n 628
 
8.2%
D 314
 
4.1%
' 314
 
4.1%
t 314
 
4.1%
314
 
4.1%
Other values (2) 628
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1649
21.4%
N 1021
13.3%
Y 836
10.9%
e 836
10.9%
s 836
10.9%
n 628
 
8.2%
D 314
 
4.1%
' 314
 
4.1%
t 314
 
4.1%
314
 
4.1%
Other values (2) 628
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1649
21.4%
N 1021
13.3%
Y 836
10.9%
e 836
10.9%
s 836
10.9%
n 628
 
8.2%
D 314
 
4.1%
' 314
 
4.1%
t 314
 
4.1%
314
 
4.1%
Other values (2) 628
 
8.2%

Process (PMF) - Development Methodologies
Categorical

High correlation  Imbalance  Missing 

Distinct34
Distinct (%)1.3%
Missing4488
Missing (%)63.6%
Memory size55.3 KiB
Waterfall (incl Linear Processing & SSADM);
2106 
Rapid Application Development (RAD);
 
100
Joint Application Development (JAD);
 
75
Multifunctional Teams;
 
73
Agile Development;
 
66
Other values (29)
 
150

Length

Max length105
Median length43
Mean length41.538911
Min length5

Characters and Unicode

Total characters106755
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.4%

Sample

1st rowWaterfall (incl Linear Processing & SSADM);
2nd rowWaterfall (incl Linear Processing & SSADM);
3rd rowWaterfall (incl Linear Processing & SSADM);
4th rowWaterfall (incl Linear Processing & SSADM);
5th rowWaterfall (incl Linear Processing & SSADM);

Common Values

ValueCountFrequency (%)
Waterfall (incl Linear Processing & SSADM); 2106
29.8%
Rapid Application Development (RAD); 100
 
1.4%
Joint Application Development (JAD); 75
 
1.1%
Multifunctional Teams; 73
 
1.0%
Agile Development; 66
 
0.9%
Joint Application Development (JAD);Multifunctional Teams; 32
 
0.5%
Joint Application Development (JAD);Rapid Application Development (RAD); 24
 
0.3%
Timeboxing; 19
 
0.3%
Incremental; 9
 
0.1%
Agile Development;Personal Software Process (PSP);Unified Process; 6
 
0.1%
Other values (24) 60
 
0.9%
(Missing) 4488
63.6%

Length

2025-05-15T14:02:20.837056image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
incl 2111
14.8%
ssadm 2111
14.8%
linear 2111
14.8%
processing 2111
14.8%
2111
14.8%
waterfall 2106
14.8%
development 354
 
2.5%
application 288
 
2.0%
joint 144
 
1.0%
rad 134
 
0.9%
Other values (36) 678
 
4.8%

Most occurring characters

ValueCountFrequency (%)
11689
 
10.9%
e 7778
 
7.3%
i 7657
 
7.2%
n 7482
 
7.0%
l 7350
 
6.9%
a 7073
 
6.6%
r 6414
 
6.0%
c 4687
 
4.4%
s 4434
 
4.2%
S 4252
 
4.0%
Other values (29) 37939
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11689
 
10.9%
e 7778
 
7.3%
i 7657
 
7.2%
n 7482
 
7.0%
l 7350
 
6.9%
a 7073
 
6.6%
r 6414
 
6.0%
c 4687
 
4.4%
s 4434
 
4.2%
S 4252
 
4.0%
Other values (29) 37939
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11689
 
10.9%
e 7778
 
7.3%
i 7657
 
7.2%
n 7482
 
7.0%
l 7350
 
6.9%
a 7073
 
6.6%
r 6414
 
6.0%
c 4687
 
4.4%
s 4434
 
4.2%
S 4252
 
4.0%
Other values (29) 37939
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11689
 
10.9%
e 7778
 
7.3%
i 7657
 
7.2%
n 7482
 
7.0%
l 7350
 
6.9%
a 7073
 
6.6%
r 6414
 
6.0%
c 4687
 
4.4%
s 4434
 
4.2%
S 4252
 
4.0%
Other values (29) 37939
35.5%

Process (PMF) - Prototyping Used
Boolean

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing6043
Missing (%)85.6%
Memory size13.9 KiB
True
1015 
(Missing)
6043 
ValueCountFrequency (%)
True 1015
 
14.4%
(Missing) 6043
85.6%
2025-05-15T14:02:20.959985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Process (PMF) - Docs
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1160385
Minimum0
Maximum19
Zeros90
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:21.127663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile11
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0316914
Coefficient of variation (CV)0.73655564
Kurtosis5.143464
Mean4.1160385
Median Absolute Deviation (MAD)1
Skewness2.2593934
Sum29051
Variance9.1911527
MonotonicityNot monotonic
2025-05-15T14:02:21.283695image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 2616
37.1%
2 1760
24.9%
4 944
 
13.4%
8 472
 
6.7%
5 422
 
6.0%
6 177
 
2.5%
14 115
 
1.6%
15 99
 
1.4%
9 95
 
1.3%
0 90
 
1.3%
Other values (10) 268
 
3.8%
ValueCountFrequency (%)
0 90
 
1.3%
1 25
 
0.4%
2 1760
24.9%
3 2616
37.1%
4 944
 
13.4%
5 422
 
6.0%
6 177
 
2.5%
7 34
 
0.5%
8 472
 
6.7%
9 95
 
1.3%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 15
 
0.2%
17 9
 
0.1%
16 38
 
0.5%
15 99
1.4%
14 115
1.6%
13 41
 
0.6%
12 33
 
0.5%
11 47
0.7%
10 25
 
0.4%

Tech (TF) - Architecture
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.1%
Missing4323
Missing (%)61.2%
Memory size55.3 KiB
Yes
1681 
No
725 
Don't Know
326 
Not Applicable
 
3

Length

Max length14
Median length3
Mean length3.5813528
Min length2

Characters and Unicode

Total characters9795
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowDon't Know
5th rowNo

Common Values

ValueCountFrequency (%)
Yes 1681
 
23.8%
No 725
 
10.3%
Don't Know 326
 
4.6%
Not Applicable 3
 
< 0.1%
(Missing) 4323
61.2%

Length

2025-05-15T14:02:21.472571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:21.706158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
yes 1681
54.9%
no 725
23.7%
don't 326
 
10.6%
know 326
 
10.6%
not 3
 
0.1%
applicable 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 1684
17.2%
Y 1681
17.2%
s 1681
17.2%
o 1380
14.1%
N 728
7.4%
n 652
 
6.7%
t 329
 
3.4%
329
 
3.4%
' 326
 
3.3%
D 326
 
3.3%
Other values (9) 679
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1684
17.2%
Y 1681
17.2%
s 1681
17.2%
o 1380
14.1%
N 728
7.4%
n 652
 
6.7%
t 329
 
3.4%
329
 
3.4%
' 326
 
3.3%
D 326
 
3.3%
Other values (9) 679
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1684
17.2%
Y 1681
17.2%
s 1681
17.2%
o 1380
14.1%
N 728
7.4%
n 652
 
6.7%
t 329
 
3.4%
329
 
3.4%
' 326
 
3.3%
D 326
 
3.3%
Other values (9) 679
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1684
17.2%
Y 1681
17.2%
s 1681
17.2%
o 1380
14.1%
N 728
7.4%
n 652
 
6.7%
t 329
 
3.4%
329
 
3.4%
' 326
 
3.3%
D 326
 
3.3%
Other values (9) 679
6.9%
Distinct60
Distinct (%)21.5%
Missing6779
Missing (%)96.0%
Memory size55.3 KiB
2025-05-15T14:02:21.973561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length166
Median length112
Mean length65.648746
Min length10

Characters and Unicode

Total characters18316
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)9.3%

Sample

1st rowRun a computer-human interface;Data entry & validation;Web/HTML browser;
2nd rowWeb/HTML browser;
3rd rowRun a computer-human interface;Business logic or rule processing;Data entry & validation;Data retrieval & presentation;Web/HTML browser;Web public interface;
4th rowWeb/HTML browser;
5th rowRun a computer-human interface;Data entry & validation;Data retrieval & presentation;
ValueCountFrequency (%)
299
14.4%
retrieval 157
 
7.5%
entry 142
 
6.8%
run 118
 
5.7%
a 118
 
5.7%
computer-human 118
 
5.7%
validation;data 105
 
5.0%
browser 103
 
4.9%
presentation 94
 
4.5%
or 86
 
4.1%
Other values (41) 741
35.6%
2025-05-15T14:02:22.420991image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1881
 
10.3%
1802
 
9.8%
a 1618
 
8.8%
r 1433
 
7.8%
t 1382
 
7.5%
n 1209
 
6.6%
i 1115
 
6.1%
o 821
 
4.5%
s 720
 
3.9%
; 706
 
3.9%
Other values (28) 5629
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18316
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1881
 
10.3%
1802
 
9.8%
a 1618
 
8.8%
r 1433
 
7.8%
t 1382
 
7.5%
n 1209
 
6.6%
i 1115
 
6.1%
o 821
 
4.5%
s 720
 
3.9%
; 706
 
3.9%
Other values (28) 5629
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18316
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1881
 
10.3%
1802
 
9.8%
a 1618
 
8.8%
r 1433
 
7.8%
t 1382
 
7.5%
n 1209
 
6.6%
i 1115
 
6.1%
o 821
 
4.5%
s 720
 
3.9%
; 706
 
3.9%
Other values (28) 5629
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18316
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1881
 
10.3%
1802
 
9.8%
a 1618
 
8.8%
r 1433
 
7.8%
t 1382
 
7.5%
n 1209
 
6.6%
i 1115
 
6.1%
o 821
 
4.5%
s 720
 
3.9%
; 706
 
3.9%
Other values (28) 5629
30.7%
Distinct81
Distinct (%)30.3%
Missing6791
Missing (%)96.2%
Memory size55.3 KiB
2025-05-15T14:02:22.706371image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length157
Median length103
Mean length56.737828
Min length9

Characters and Unicode

Total characters15149
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)15.0%

Sample

1st rowDatabase server;HTML/web server;Mail server;Security/authentication;
2nd rowDatabase server;
3rd rowDatabase server;Mail server;Security/authentication;
4th rowDatabase server;FTP server;HTML/web server;Mail server;Messaging server;Object/component server;Security/authentication;
5th rowDatabase server;FTP server;HTML/web server;Mail server;Security/authentication;
ValueCountFrequency (%)
database 218
20.2%
server;security/authentication 115
10.7%
server;html/web 113
10.5%
server 82
 
7.6%
legacy 55
 
5.1%
server;file 51
 
4.7%
or 51
 
4.7%
print 51
 
4.7%
server;object/component 48
 
4.4%
server;multi-user 44
 
4.1%
Other values (38) 251
23.3%
2025-05-15T14:02:23.165186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2134
14.1%
r 1474
 
9.7%
a 1211
 
8.0%
t 1088
 
7.2%
s 945
 
6.2%
812
 
5.4%
i 805
 
5.3%
; 798
 
5.3%
v 586
 
3.9%
n 567
 
3.7%
Other values (38) 4729
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2134
14.1%
r 1474
 
9.7%
a 1211
 
8.0%
t 1088
 
7.2%
s 945
 
6.2%
812
 
5.4%
i 805
 
5.3%
; 798
 
5.3%
v 586
 
3.9%
n 567
 
3.7%
Other values (38) 4729
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2134
14.1%
r 1474
 
9.7%
a 1211
 
8.0%
t 1088
 
7.2%
s 945
 
6.2%
812
 
5.4%
i 805
 
5.3%
; 798
 
5.3%
v 586
 
3.9%
n 567
 
3.7%
Other values (38) 4729
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2134
14.1%
r 1474
 
9.7%
a 1211
 
8.0%
t 1088
 
7.2%
s 945
 
6.2%
812
 
5.4%
i 805
 
5.3%
; 798
 
5.3%
v 586
 
3.9%
n 567
 
3.7%
Other values (38) 4729
31.2%

Tech (TF) - Server Roles
Categorical

High correlation  Missing 

Distinct42
Distinct (%)3.1%
Missing5693
Missing (%)80.7%
Memory size55.3 KiB
Client server;
291 
C/S;
289 
Client Server;
236 
Web;
182 
Mainframe;
69 
Other values (37)
298 

Length

Max length62
Median length59
Mean length14.872527
Min length4

Characters and Unicode

Total characters20301
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.9%

Sample

1st rowC/S;
2nd rowC/S;
3rd rowC/S;
4th rowC/S;
5th rowClient Server;

Common Values

ValueCountFrequency (%)
Client server; 291
 
4.1%
C/S; 289
 
4.1%
Client Server; 236
 
3.3%
Web; 182
 
2.6%
Mainframe; 69
 
1.0%
Unix;Client: presentation, processing; 46
 
0.7%
Browser-server Architecture; 34
 
0.5%
Unix;Client: presentation; Server: processing; 26
 
0.4%
Multi-tier with web public interface; 24
 
0.3%
Stand alone; 21
 
0.3%
Other values (32) 147
 
2.1%
(Missing) 5693
80.7%

Length

2025-05-15T14:02:23.435869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
server 610
23.8%
client 532
20.8%
c/s 289
11.3%
web 206
 
8.0%
presentation 156
 
6.1%
processing 156
 
6.1%
unix;client 84
 
3.3%
mainframe 69
 
2.7%
architecture 46
 
1.8%
lan 44
 
1.7%
Other values (38) 369
14.4%

Most occurring characters

ValueCountFrequency (%)
e 3123
15.4%
r 2023
 
10.0%
; 1604
 
7.9%
n 1485
 
7.3%
i 1409
 
6.9%
t 1284
 
6.3%
1196
 
5.9%
C 985
 
4.9%
s 907
 
4.5%
l 776
 
3.8%
Other values (39) 5509
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3123
15.4%
r 2023
 
10.0%
; 1604
 
7.9%
n 1485
 
7.3%
i 1409
 
6.9%
t 1284
 
6.3%
1196
 
5.9%
C 985
 
4.9%
s 907
 
4.5%
l 776
 
3.8%
Other values (39) 5509
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3123
15.4%
r 2023
 
10.0%
; 1604
 
7.9%
n 1485
 
7.3%
i 1409
 
6.9%
t 1284
 
6.3%
1196
 
5.9%
C 985
 
4.9%
s 907
 
4.5%
l 776
 
3.8%
Other values (39) 5509
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3123
15.4%
r 2023
 
10.0%
; 1604
 
7.9%
n 1485
 
7.3%
i 1409
 
6.9%
t 1284
 
6.3%
1196
 
5.9%
C 985
 
4.9%
s 907
 
4.5%
l 776
 
3.8%
Other values (39) 5509
27.1%

Tech (TF) - Type of Server
Categorical

High correlation  Missing 

Distinct9
Distinct (%)1.5%
Missing6474
Missing (%)91.7%
Memory size55.3 KiB
Client server;
291 
Unix;
89 
Mainframe;
86 
LAN Based;
44 
Multi-tier with web public interface;
 
24
Other values (4)
50 

Length

Max length37
Median length21
Mean length12.77911
Min length5

Characters and Unicode

Total characters7463
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClient server;
2nd rowLAN Based;
3rd rowStand alone;
4th rowUnix;
5th rowProprietary Midrange;

Common Values

ValueCountFrequency (%)
Client server; 291
 
4.1%
Unix; 89
 
1.3%
Mainframe; 86
 
1.2%
LAN Based; 44
 
0.6%
Multi-tier with web public interface; 24
 
0.3%
Stand alone; 21
 
0.3%
Proprietary Midrange; 20
 
0.3%
back-end; 6
 
0.1%
webserver; 3
 
< 0.1%
(Missing) 6474
91.7%

Length

2025-05-15T14:02:23.630737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:23.912323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
client 291
27.6%
server 291
27.6%
unix 89
 
8.4%
mainframe 86
 
8.1%
lan 44
 
4.2%
based 44
 
4.2%
interface 24
 
2.3%
public 24
 
2.3%
web 24
 
2.3%
with 24
 
2.3%
Other values (7) 115
 
10.9%

Most occurring characters

ValueCountFrequency (%)
e 1175
15.7%
r 802
10.7%
i 626
 
8.4%
; 584
 
7.8%
n 558
 
7.5%
472
 
6.3%
t 428
 
5.7%
l 360
 
4.8%
s 338
 
4.5%
a 328
 
4.4%
Other values (25) 1792
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1175
15.7%
r 802
10.7%
i 626
 
8.4%
; 584
 
7.8%
n 558
 
7.5%
472
 
6.3%
t 428
 
5.7%
l 360
 
4.8%
s 338
 
4.5%
a 328
 
4.4%
Other values (25) 1792
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1175
15.7%
r 802
10.7%
i 626
 
8.4%
; 584
 
7.8%
n 558
 
7.5%
472
 
6.3%
t 428
 
5.7%
l 360
 
4.8%
s 338
 
4.5%
a 328
 
4.4%
Other values (25) 1792
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1175
15.7%
r 802
10.7%
i 626
 
8.4%
; 584
 
7.8%
n 558
 
7.5%
472
 
6.3%
t 428
 
5.7%
l 360
 
4.8%
s 338
 
4.5%
a 328
 
4.4%
Other values (25) 1792
24.0%

Tech (TF) - Client/Server Description
Categorical

High correlation  Missing 

Distinct25
Distinct (%)2.7%
Missing6115
Missing (%)86.6%
Memory size55.3 KiB
C/S;
289 
Client Server;
236 
Web;
182 
Client: presentation; Server: processing;
75 
Client: presentation, processing;
63 
Other values (20)
98 

Length

Max length55
Median length4
Mean length13.613998
Min length4

Characters and Unicode

Total characters12838
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)1.1%

Sample

1st rowC/S;
2nd rowC/S;
3rd rowC/S;
4th rowC/S;
5th rowClient Server;

Common Values

ValueCountFrequency (%)
C/S; 289
 
4.1%
Client Server; 236
 
3.3%
Web; 182
 
2.6%
Client: presentation; Server: processing; 75
 
1.1%
Client: presentation, processing; 63
 
0.9%
Browser-server Architecture; 34
 
0.5%
Client: presentation, processing, data; 16
 
0.2%
Client-server Architecture; 11
 
0.2%
Stand-alone; 9
 
0.1%
Presentation & Logic on server; 6
 
0.1%
Other values (15) 22
 
0.3%
(Missing) 6115
86.6%

Length

2025-05-15T14:02:24.213669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
client 393
23.6%
server 319
19.1%
c/s 289
17.3%
web 182
10.9%
presentation 163
9.8%
processing 156
 
9.4%
architecture 46
 
2.8%
browser-server 34
 
2.0%
data 18
 
1.1%
client-server 13
 
0.8%
Other values (20) 54
 
3.2%

Most occurring characters

ValueCountFrequency (%)
e 1948
15.2%
r 1221
 
9.5%
; 1020
 
7.9%
n 927
 
7.2%
t 856
 
6.7%
i 783
 
6.1%
724
 
5.6%
C 694
 
5.4%
S 610
 
4.8%
s 569
 
4.4%
Other values (35) 3486
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1948
15.2%
r 1221
 
9.5%
; 1020
 
7.9%
n 927
 
7.2%
t 856
 
6.7%
i 783
 
6.1%
724
 
5.6%
C 694
 
5.4%
S 610
 
4.8%
s 569
 
4.4%
Other values (35) 3486
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1948
15.2%
r 1221
 
9.5%
; 1020
 
7.9%
n 927
 
7.2%
t 856
 
6.7%
i 783
 
6.1%
724
 
5.6%
C 694
 
5.4%
S 610
 
4.8%
s 569
 
4.4%
Other values (35) 3486
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1948
15.2%
r 1221
 
9.5%
; 1020
 
7.9%
n 927
 
7.2%
t 856
 
6.7%
i 783
 
6.1%
724
 
5.6%
C 694
 
5.4%
S 610
 
4.8%
s 569
 
4.4%
Other values (35) 3486
27.2%

Tech (TF) - Web Development
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.2%
Missing6054
Missing (%)85.8%
Memory size55.3 KiB
Web
1002 
Web?
 
2

Length

Max length4
Median length3
Mean length3.001992
Min length3

Characters and Unicode

Total characters3014
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb

Common Values

ValueCountFrequency (%)
Web 1002
 
14.2%
Web? 2
 
< 0.1%
(Missing) 6054
85.8%

Length

2025-05-15T14:02:24.508725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:24.723787image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
web 1004
100.0%

Most occurring characters

ValueCountFrequency (%)
W 1004
33.3%
e 1004
33.3%
b 1004
33.3%
? 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 1004
33.3%
e 1004
33.3%
b 1004
33.3%
? 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 1004
33.3%
e 1004
33.3%
b 1004
33.3%
? 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 1004
33.3%
e 1004
33.3%
b 1004
33.3%
? 2
 
0.1%

Tech (TF) - DBMS Used
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing3735
Missing (%)52.9%
Memory size13.9 KiB
True
3269 
False
 
54
(Missing)
3735 
ValueCountFrequency (%)
True 3269
46.3%
False 54
 
0.8%
(Missing) 3735
52.9%
2025-05-15T14:02:24.928385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Tech (TF) - Tools Used
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct13
Distinct (%)0.3%
Missing2618
Missing (%)37.1%
Infinite0
Infinite (%)0.0%
Mean0.92815315
Minimum0
Maximum17
Zeros3063
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:25.119755image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.731991
Coefficient of variation (CV)1.8660617
Kurtosis6.4876061
Mean0.92815315
Median Absolute Deviation (MAD)0
Skewness2.3313639
Sum4121
Variance2.9997929
MonotonicityNot monotonic
2025-05-15T14:02:25.405824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 3063
43.4%
2 573
 
8.1%
4 285
 
4.0%
1 213
 
3.0%
3 120
 
1.7%
6 48
 
0.7%
8 47
 
0.7%
5 41
 
0.6%
7 35
 
0.5%
9 10
 
0.1%
Other values (3) 5
 
0.1%
(Missing) 2618
37.1%
ValueCountFrequency (%)
0 3063
43.4%
1 213
 
3.0%
2 573
 
8.1%
3 120
 
1.7%
4 285
 
4.0%
5 41
 
0.6%
6 48
 
0.7%
7 35
 
0.5%
8 47
 
0.7%
9 10
 
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 10
 
0.1%
8 47
 
0.7%
7 35
 
0.5%
6 48
 
0.7%
5 41
 
0.6%
4 285
4.0%
3 120
1.7%

People (PRF) - Project user involvement
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.7%
Missing6380
Missing (%)90.4%
Memory size55.3 KiB
Yes
477 
Don't Know
144 
No
54 
Best
 
2
Low
 
1

Length

Max length10
Median length3
Mean length4.4100295
Min length2

Characters and Unicode

Total characters2990
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowDon't Know
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 477
 
6.8%
Don't Know 144
 
2.0%
No 54
 
0.8%
Best 2
 
< 0.1%
Low 1
 
< 0.1%
(Missing) 6380
90.4%

Length

2025-05-15T14:02:25.725252image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:25.980088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
yes 477
58.0%
don't 144
 
17.5%
know 144
 
17.5%
no 54
 
6.6%
best 2
 
0.2%
low 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 479
16.0%
s 479
16.0%
Y 477
16.0%
o 343
11.5%
n 288
9.6%
t 146
 
4.9%
w 145
 
4.8%
D 144
 
4.8%
' 144
 
4.8%
144
 
4.8%
Other values (4) 201
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 479
16.0%
s 479
16.0%
Y 477
16.0%
o 343
11.5%
n 288
9.6%
t 146
 
4.9%
w 145
 
4.8%
D 144
 
4.8%
' 144
 
4.8%
144
 
4.8%
Other values (4) 201
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 479
16.0%
s 479
16.0%
Y 477
16.0%
o 343
11.5%
n 288
9.6%
t 146
 
4.9%
w 145
 
4.8%
D 144
 
4.8%
' 144
 
4.8%
144
 
4.8%
Other values (4) 201
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 479
16.0%
s 479
16.0%
Y 477
16.0%
o 343
11.5%
n 288
9.6%
t 146
 
4.9%
w 145
 
4.8%
D 144
 
4.8%
' 144
 
4.8%
144
 
4.8%
Other values (4) 201
6.7%

People (PRF) - BA team experience <1 yr
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct22
Distinct (%)8.0%
Missing6784
Missing (%)96.1%
Infinite0
Infinite (%)0.0%
Mean3.0620438
Minimum0
Maximum31
Zeros79
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:26.227915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q34
95-th percentile11.7
Maximum31
Range31
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.627496
Coefficient of variation (CV)1.5112442
Kurtosis12.792639
Mean3.0620438
Median Absolute Deviation (MAD)1.5
Skewness3.1689444
Sum839
Variance21.413719
MonotonicityNot monotonic
2025-05-15T14:02:26.479899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 79
 
1.1%
1 58
 
0.8%
2 38
 
0.5%
4 22
 
0.3%
3 20
 
0.3%
5 13
 
0.2%
6 11
 
0.2%
7 7
 
0.1%
8 4
 
0.1%
9 4
 
0.1%
Other values (12) 18
 
0.3%
(Missing) 6784
96.1%
ValueCountFrequency (%)
0 79
1.1%
1 58
0.8%
2 38
0.5%
3 20
 
0.3%
4 22
 
0.3%
5 13
 
0.2%
6 11
 
0.2%
7 7
 
0.1%
8 4
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
31 1
< 0.1%
30 1
< 0.1%
27 1
< 0.1%
23 1
< 0.1%
19 2
< 0.1%
17 1
< 0.1%
16 1
< 0.1%
15 2
< 0.1%
14 2
< 0.1%
13 2
< 0.1%

People (PRF) - BA team experience 1 to 3 yr
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct17
Distinct (%)5.8%
Missing6764
Missing (%)95.8%
Infinite0
Infinite (%)0.0%
Mean2.7244898
Minimum0
Maximum40
Zeros74
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:26.783833image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median2
Q33
95-th percentile7
Maximum40
Range40
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation4.5119045
Coefficient of variation (CV)1.6560548
Kurtosis38.469734
Mean2.7244898
Median Absolute Deviation (MAD)1.5
Skewness5.4812788
Sum801
Variance20.357282
MonotonicityNot monotonic
2025-05-15T14:02:27.026760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 74
 
1.0%
1 62
 
0.9%
2 51
 
0.7%
3 34
 
0.5%
4 23
 
0.3%
5 21
 
0.3%
6 11
 
0.2%
7 5
 
0.1%
10 3
 
< 0.1%
8 2
 
< 0.1%
Other values (7) 8
 
0.1%
(Missing) 6764
95.8%
ValueCountFrequency (%)
0 74
1.0%
1 62
0.9%
2 51
0.7%
3 34
0.5%
4 23
 
0.3%
5 21
 
0.3%
6 11
 
0.2%
7 5
 
0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
40 2
 
< 0.1%
32 1
 
< 0.1%
25 1
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
10 3
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 5
0.1%

People (PRF) - BA team experience >3 yr
Real number (ℝ)

High correlation  Missing 

Distinct20
Distinct (%)5.5%
Missing6697
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean3.9972299
Minimum0
Maximum25
Zeros39
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:27.283687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile11
Maximum25
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8344191
Coefficient of variation (CV)0.9592691
Kurtosis6.3844267
Mean3.9972299
Median Absolute Deviation (MAD)2
Skewness2.0848594
Sum1443
Variance14.70277
MonotonicityNot monotonic
2025-05-15T14:02:27.550188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 62
 
0.9%
1 54
 
0.8%
3 47
 
0.7%
4 41
 
0.6%
0 39
 
0.6%
5 36
 
0.5%
6 23
 
0.3%
10 12
 
0.2%
7 9
 
0.1%
8 9
 
0.1%
Other values (10) 29
 
0.4%
(Missing) 6697
94.9%
ValueCountFrequency (%)
0 39
0.6%
1 54
0.8%
2 62
0.9%
3 47
0.7%
4 41
0.6%
5 36
0.5%
6 23
 
0.3%
7 9
 
0.1%
8 9
 
0.1%
9 7
 
0.1%
ValueCountFrequency (%)
25 2
 
< 0.1%
20 2
 
< 0.1%
18 1
 
< 0.1%
16 2
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 3
 
< 0.1%
11 6
0.1%
10 12
0.2%

People (PRF) - IT experience <1 yr
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)19.3%
Missing7001
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean2.2807018
Minimum0
Maximum24
Zeros24
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:27.752581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile11.2
Maximum24
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.5029926
Coefficient of variation (CV)1.9743891
Kurtosis11.801909
Mean2.2807018
Median Absolute Deviation (MAD)1
Skewness3.3054838
Sum130
Variance20.276942
MonotonicityNot monotonic
2025-05-15T14:02:27.973622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 24
 
0.3%
1 16
 
0.2%
3 5
 
0.1%
2 3
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
16 1
 
< 0.1%
24 1
 
< 0.1%
10 1
 
< 0.1%
17 1
 
< 0.1%
(Missing) 7001
99.2%
ValueCountFrequency (%)
0 24
0.3%
1 16
0.2%
2 3
 
< 0.1%
3 5
 
0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
10 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
10 1
 
< 0.1%
6 2
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
3 5
 
0.1%
2 3
 
< 0.1%
1 16
0.2%

People (PRF) - IT experience 1 to 3 yr
Real number (ℝ)

High correlation  Missing 

Distinct15
Distinct (%)22.1%
Missing6990
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean4.2205882
Minimum0
Maximum61
Zeros19
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:28.229368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35.25
95-th percentile14.25
Maximum61
Range61
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation8.1567582
Coefficient of variation (CV)1.9326117
Kurtosis35.485567
Mean4.2205882
Median Absolute Deviation (MAD)2
Skewness5.3209295
Sum287
Variance66.532704
MonotonicityNot monotonic
2025-05-15T14:02:28.457088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 19
 
0.3%
1 14
 
0.2%
2 8
 
0.1%
4 5
 
0.1%
3 4
 
0.1%
7 4
 
0.1%
10 3
 
< 0.1%
9 3
 
< 0.1%
16 2
 
< 0.1%
8 1
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 6990
99.0%
ValueCountFrequency (%)
0 19
0.3%
1 14
0.2%
2 8
0.1%
3 4
 
0.1%
4 5
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 4
 
0.1%
8 1
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
61 1
 
< 0.1%
17 1
 
< 0.1%
16 2
< 0.1%
11 1
 
< 0.1%
10 3
< 0.1%
9 3
< 0.1%
8 1
 
< 0.1%
7 4
0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%

People (PRF) - IT experience >3 yr
Real number (ℝ)

High correlation  Missing 

Distinct17
Distinct (%)20.7%
Missing6976
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean6.3902439
Minimum0
Maximum87
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:28.666395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36.75
95-th percentile14
Maximum87
Range87
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation12.863726
Coefficient of variation (CV)2.0130259
Kurtosis32.454968
Mean6.3902439
Median Absolute Deviation (MAD)2
Skewness5.5700625
Sum524
Variance165.47546
MonotonicityNot monotonic
2025-05-15T14:02:28.862168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 13
 
0.2%
3 13
 
0.2%
4 9
 
0.1%
5 8
 
0.1%
2 7
 
0.1%
0 6
 
0.1%
6 5
 
0.1%
8 5
 
0.1%
7 5
 
0.1%
10 3
 
< 0.1%
Other values (7) 8
 
0.1%
(Missing) 6976
98.8%
ValueCountFrequency (%)
0 6
0.1%
1 13
0.2%
2 7
0.1%
3 13
0.2%
4 9
0.1%
5 8
0.1%
6 5
 
0.1%
7 5
 
0.1%
8 5
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
87 1
 
< 0.1%
81 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
11 1
 
< 0.1%
10 3
< 0.1%
9 1
 
< 0.1%
8 5
0.1%
7 5
0.1%

People (PRF) - IT experience <3 yr
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct18
Distinct (%)6.6%
Missing6785
Missing (%)96.1%
Infinite0
Infinite (%)0.0%
Mean2.9120879
Minimum0
Maximum36
Zeros73
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:29.114149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum36
Range36
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.601746
Coefficient of variation (CV)1.5802222
Kurtosis20.51214
Mean2.9120879
Median Absolute Deviation (MAD)1
Skewness3.976124
Sum795
Variance21.176067
MonotonicityNot monotonic
2025-05-15T14:02:29.315218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 73
 
1.0%
1 65
 
0.9%
2 35
 
0.5%
3 26
 
0.4%
4 21
 
0.3%
5 15
 
0.2%
6 13
 
0.2%
7 6
 
0.1%
9 5
 
0.1%
24 3
 
< 0.1%
Other values (8) 11
 
0.2%
(Missing) 6785
96.1%
ValueCountFrequency (%)
0 73
1.0%
1 65
0.9%
2 35
0.5%
3 26
 
0.4%
4 21
 
0.3%
5 15
 
0.2%
6 13
 
0.2%
7 6
 
0.1%
8 1
 
< 0.1%
9 5
 
0.1%
ValueCountFrequency (%)
36 1
 
< 0.1%
33 1
 
< 0.1%
24 3
< 0.1%
20 1
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
11 2
 
< 0.1%
10 2
 
< 0.1%
9 5
0.1%
8 1
 
< 0.1%

People (PRF) - IT experience 3 to 9 yr
Real number (ℝ)

High correlation  Missing 

Distinct18
Distinct (%)5.3%
Missing6717
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean3.5366569
Minimum0
Maximum40
Zeros24
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:29.480346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile10
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.2387539
Coefficient of variation (CV)1.19852
Kurtosis37.764536
Mean3.5366569
Median Absolute Deviation (MAD)2
Skewness5.1128258
Sum1206
Variance17.967035
MonotonicityNot monotonic
2025-05-15T14:02:29.690484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 80
 
1.1%
2 63
 
0.9%
3 50
 
0.7%
4 40
 
0.6%
5 32
 
0.5%
0 24
 
0.3%
10 15
 
0.2%
6 15
 
0.2%
7 9
 
0.1%
12 2
 
< 0.1%
Other values (8) 11
 
0.2%
(Missing) 6717
95.2%
ValueCountFrequency (%)
0 24
 
0.3%
1 80
1.1%
2 63
0.9%
3 50
0.7%
4 40
0.6%
5 32
 
0.5%
6 15
 
0.2%
7 9
 
0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
40 2
 
< 0.1%
33 1
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 15
0.2%
9 2
 
< 0.1%
8 2
 
< 0.1%

People (PRF) - IT experience >9 yr
Real number (ℝ)

High correlation  Missing 

Distinct22
Distinct (%)7.4%
Missing6761
Missing (%)95.8%
Infinite0
Infinite (%)0.0%
Mean4.8653199
Minimum0
Maximum25
Zeros26
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:29.880687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q36
95-th percentile15
Maximum25
Range25
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.6841498
Coefficient of variation (CV)0.96276297
Kurtosis2.8120763
Mean4.8653199
Median Absolute Deviation (MAD)3
Skewness1.6293811
Sum1445
Variance21.941259
MonotonicityNot monotonic
2025-05-15T14:02:30.149249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 51
 
0.7%
3 39
 
0.6%
4 34
 
0.5%
2 32
 
0.5%
0 26
 
0.4%
5 24
 
0.3%
6 17
 
0.2%
9 12
 
0.2%
10 11
 
0.2%
8 10
 
0.1%
Other values (12) 41
 
0.6%
(Missing) 6761
95.8%
ValueCountFrequency (%)
0 26
0.4%
1 51
0.7%
2 32
0.5%
3 39
0.6%
4 34
0.5%
5 24
0.3%
6 17
 
0.2%
7 10
 
0.1%
8 10
 
0.1%
9 12
 
0.2%
ValueCountFrequency (%)
25 1
 
< 0.1%
24 1
 
< 0.1%
21 1
 
< 0.1%
20 3
 
< 0.1%
18 3
 
< 0.1%
17 1
 
< 0.1%
15 8
0.1%
14 4
0.1%
13 4
0.1%
12 3
 
< 0.1%

People (PRF) - Project manage experience
Real number (ℝ)

High correlation  Missing 

Distinct34
Distinct (%)8.0%
Missing6633
Missing (%)94.0%
Infinite0
Infinite (%)0.0%
Mean12.181176
Minimum0
Maximum200
Zeros13
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:30.449680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q315
95-th percentile35
Maximum200
Range200
Interquartile range (IQR)11

Descriptive statistics

Standard deviation17.407915
Coefficient of variation (CV)1.4290832
Kurtosis40.776407
Mean12.181176
Median Absolute Deviation (MAD)5
Skewness5.3151481
Sum5177
Variance303.03549
MonotonicityNot monotonic
2025-05-15T14:02:30.690603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10 61
 
0.9%
5 48
 
0.7%
3 33
 
0.5%
2 29
 
0.4%
6 27
 
0.4%
20 26
 
0.4%
15 25
 
0.4%
4 22
 
0.3%
1 21
 
0.3%
8 19
 
0.3%
Other values (24) 114
 
1.6%
(Missing) 6633
94.0%
ValueCountFrequency (%)
0 13
 
0.2%
1 21
0.3%
2 29
0.4%
3 33
0.5%
4 22
0.3%
5 48
0.7%
6 27
0.4%
7 13
 
0.2%
8 19
 
0.3%
9 1
 
< 0.1%
ValueCountFrequency (%)
200 1
 
< 0.1%
100 7
0.1%
50 6
0.1%
45 2
 
< 0.1%
40 3
< 0.1%
35 5
0.1%
32 2
 
< 0.1%
30 6
0.1%
28 1
 
< 0.1%
26 1
 
< 0.1%

People (PRF) - Project manage changes
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.9%
Missing6481
Missing (%)91.8%
Memory size55.3 KiB
0.0
462 
1.0
91 
2.0
 
15
3.0
 
5
4.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1731
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 462
 
6.5%
1.0 91
 
1.3%
2.0 15
 
0.2%
3.0 5
 
0.1%
4.0 4
 
0.1%
(Missing) 6481
91.8%

Length

2025-05-15T14:02:30.976914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:31.214456image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 462
80.1%
1.0 91
 
15.8%
2.0 15
 
2.6%
3.0 5
 
0.9%
4.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1039
60.0%
. 577
33.3%
1 91
 
5.3%
2 15
 
0.9%
3 5
 
0.3%
4 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1039
60.0%
. 577
33.3%
1 91
 
5.3%
2 15
 
0.9%
3 5
 
0.3%
4 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1039
60.0%
. 577
33.3%
1 91
 
5.3%
2 15
 
0.9%
3 5
 
0.3%
4 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1039
60.0%
. 577
33.3%
1 91
 
5.3%
2 15
 
0.9%
3 5
 
0.3%
4 4
 
0.2%

People (PRF) - Personnel changes
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct15
Distinct (%)1.9%
Missing6287
Missing (%)89.1%
Infinite0
Infinite (%)0.0%
Mean0.89494163
Minimum0
Maximum16
Zeros452
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:31.474221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7568014
Coefficient of variation (CV)1.9630346
Kurtosis24.058942
Mean0.89494163
Median Absolute Deviation (MAD)0
Skewness4.1979111
Sum690
Variance3.086351
MonotonicityNot monotonic
2025-05-15T14:02:31.768961image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 452
 
6.4%
1 170
 
2.4%
2 60
 
0.9%
3 60
 
0.9%
4 8
 
0.1%
5 5
 
0.1%
11 3
 
< 0.1%
12 3
 
< 0.1%
10 2
 
< 0.1%
7 2
 
< 0.1%
Other values (5) 6
 
0.1%
(Missing) 6287
89.1%
ValueCountFrequency (%)
0 452
6.4%
1 170
 
2.4%
2 60
 
0.9%
3 60
 
0.9%
4 8
 
0.1%
5 5
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 1
 
< 0.1%
12 3
< 0.1%
11 3
< 0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
5 5
0.1%

Project (PRF) - Total project cost
Real number (ℝ)

High correlation  Missing 

Distinct998
Distinct (%)97.6%
Missing6035
Missing (%)85.5%
Infinite0
Infinite (%)0.0%
Mean303714.06
Minimum560
Maximum17288808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2025-05-15T14:02:32.091114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum560
5-th percentile8113.1
Q136576
median106746
Q3295941.5
95-th percentile1091272.6
Maximum17288808
Range17288248
Interquartile range (IQR)259365.5

Descriptive statistics

Standard deviation885956.47
Coefficient of variation (CV)2.9170742
Kurtosis264.97763
Mean303714.06
Median Absolute Deviation (MAD)86988
Skewness14.415449
Sum3.1069949 × 108
Variance7.8491887 × 1011
MonotonicityNot monotonic
2025-05-15T14:02:32.420558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114160 3
 
< 0.1%
128000 3
 
< 0.1%
1680 3
 
< 0.1%
60000 3
 
< 0.1%
640 3
 
< 0.1%
8750 2
 
< 0.1%
72320 2
 
< 0.1%
23000 2
 
< 0.1%
42000 2
 
< 0.1%
13654 2
 
< 0.1%
Other values (988) 998
 
14.1%
(Missing) 6035
85.5%
ValueCountFrequency (%)
560 1
 
< 0.1%
640 3
< 0.1%
800 1
 
< 0.1%
960 1
 
< 0.1%
1099 1
 
< 0.1%
1242 1
 
< 0.1%
1440 1
 
< 0.1%
1597 1
 
< 0.1%
1600 1
 
< 0.1%
1658 1
 
< 0.1%
ValueCountFrequency (%)
17288808 2
< 0.1%
4612300 1
< 0.1%
4071933 1
< 0.1%
3723500 1
< 0.1%
3642959 1
< 0.1%
3624672 1
< 0.1%
3248000 1
< 0.1%
2860000 1
< 0.1%
2838927 1
< 0.1%
2597900 1
< 0.1%

Project (PRF) - Cost currency
Categorical

High correlation  Imbalance  Missing 

Distinct16
Distinct (%)1.4%
Missing5916
Missing (%)83.8%
Memory size55.3 KiB
United States, dollar
723 
European, euro
250 
Netherlands, florin
 
60
United Kingdom, pound sterling
 
43
Canada, dollar
 
34
Other values (11)
 
32

Length

Max length30
Median length21
Mean length19.337128
Min length3

Characters and Unicode

Total characters22083
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.7%

Sample

1st rowUnited States, dollar
2nd rowUnited States, dollar
3rd rowEuropean, euro
4th rowEuropean, euro
5th rowUnited States, dollar

Common Values

ValueCountFrequency (%)
United States, dollar 723
 
10.2%
European, euro 250
 
3.5%
Netherlands, florin 60
 
0.9%
United Kingdom, pound sterling 43
 
0.6%
Canada, dollar 34
 
0.5%
Australia, dollar 19
 
0.3%
Germany, Mark 3
 
< 0.1%
India, rupee 2
 
< 0.1%
Chinese, RMB 1
 
< 0.1%
South Africa, rand 1
 
< 0.1%
Other values (6) 6
 
0.1%
(Missing) 5916
83.8%

Length

2025-05-15T14:02:32.744596image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dollar 777
25.1%
united 766
24.8%
states 723
23.4%
european 250
 
8.1%
euro 250
 
8.1%
netherlands 60
 
1.9%
florin 60
 
1.9%
kingdom 43
 
1.4%
pound 43
 
1.4%
sterling 43
 
1.4%
Other values (21) 78
 
2.5%

Most occurring characters

ValueCountFrequency (%)
t 2338
10.6%
e 2168
9.8%
a 1970
8.9%
1951
8.8%
l 1741
 
7.9%
d 1729
 
7.8%
r 1474
 
6.7%
o 1425
 
6.5%
n 1310
 
5.9%
, 1140
 
5.2%
Other values (28) 4837
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22083
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2338
10.6%
e 2168
9.8%
a 1970
8.9%
1951
8.8%
l 1741
 
7.9%
d 1729
 
7.8%
r 1474
 
6.7%
o 1425
 
6.5%
n 1310
 
5.9%
, 1140
 
5.2%
Other values (28) 4837
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22083
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2338
10.6%
e 2168
9.8%
a 1970
8.9%
1951
8.8%
l 1741
 
7.9%
d 1729
 
7.8%
r 1474
 
6.7%
o 1425
 
6.5%
n 1310
 
5.9%
, 1140
 
5.2%
Other values (28) 4837
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22083
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2338
10.6%
e 2168
9.8%
a 1970
8.9%
1951
8.8%
l 1741
 
7.9%
d 1729
 
7.8%
r 1474
 
6.7%
o 1425
 
6.5%
n 1310
 
5.9%
, 1140
 
5.2%
Other values (28) 4837
21.9%

Project (PRF) - Currency multiple
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.4%
Missing6255
Missing (%)88.6%
Memory size55.3 KiB
No
798 
Yes 1,000
 
3
Yes 10,000
 
2

Length

Max length10
Median length2
Mean length2.0460772
Min length2

Characters and Unicode

Total characters1643
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 798
 
11.3%
Yes 1,000 3
 
< 0.1%
Yes 10,000 2
 
< 0.1%
(Missing) 6255
88.6%

Length

2025-05-15T14:02:32.935489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T14:02:33.190401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
no 798
98.8%
yes 5
 
0.6%
1,000 3
 
0.4%
10,000 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 798
48.6%
o 798
48.6%
0 17
 
1.0%
Y 5
 
0.3%
e 5
 
0.3%
s 5
 
0.3%
5
 
0.3%
1 5
 
0.3%
, 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 798
48.6%
o 798
48.6%
0 17
 
1.0%
Y 5
 
0.3%
e 5
 
0.3%
s 5
 
0.3%
5
 
0.3%
1 5
 
0.3%
, 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 798
48.6%
o 798
48.6%
0 17
 
1.0%
Y 5
 
0.3%
e 5
 
0.3%
s 5
 
0.3%
5
 
0.3%
1 5
 
0.3%
, 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 798
48.6%
o 798
48.6%
0 17
 
1.0%
Y 5
 
0.3%
e 5
 
0.3%
s 5
 
0.3%
5
 
0.3%
1 5
 
0.3%
, 5
 
0.3%

Interactions

2025-05-15T14:01:57.644187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:51.587657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:56.336677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:01.505009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:07.344017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:12.121428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:17.380854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:22.708493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:27.661218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:32.757893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:38.519259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:43.444315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:48.125119image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:53.321487image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:58.415540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:03.322355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:08.893827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:13.957322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:18.547581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:23.472956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:27.662971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:31.800462image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:37.203531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:42.420127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:46.943163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:51.958626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:57.884811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:51.803896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:56.503568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:01.743920image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:07.471458image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:12.285630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:17.608794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:22.889087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:27.841802image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:33.037292image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:38.667466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:43.608428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:48.362242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:53.540510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:58.563172image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:03.502796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:09.035169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:14.121975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:18.676806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:23.635295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:27.799712image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:31.958275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:37.419488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:42.660903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:47.117898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:52.179594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:58.048109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:51.984496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:56.653790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:02.043288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:07.651705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:12.629457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:17.823045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:23.161772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:28.036577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:33.258896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:38.838895image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:43.782658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:48.559226image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:54.022441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:58.737431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:03.722516image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:09.288627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:14.343994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:18.858550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:23.825898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:28.036545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:32.215599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:37.666070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:42.822701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:47.302531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:52.411786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:58.299518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:52.249754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:56.991347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:02.311878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:07.812013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:12.787179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:18.115837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:23.329620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:28.307498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:33.516206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:39.005045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:43.996356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:48.830767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:54.211960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:58.943089image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:03.949953image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:09.428671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:14.511853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:19.023905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:24.004709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:28.166666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:32.419236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:37.877691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:42.972215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:47.552503image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:52.739681image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:58.468167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:52.414141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:57.192567image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:02.567227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:07.986520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:12.993159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:18.349554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:23.505295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:28.554163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:33.739125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:39.162971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:44.173658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:48.998303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:54.350300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:59.130294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:04.244066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:09.644635image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:14.690492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:19.224380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:24.195879image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:28.351304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:32.597765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:38.129580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:43.128969image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:47.753915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:52.933235image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:58.663341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:52.582742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:57.384715image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:02.789919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:08.129992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:13.187285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:18.582776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:24.006655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:28.732971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:33.981639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:39.377646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:44.361368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:49.212732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:54.591750image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:59.379496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:04.440218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:09.896851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:14.874052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:19.400716image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:24.380180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:28.473963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:32.815020image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:38.332814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:43.307601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:47.961770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:53.192493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:58.864058image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:52.796420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:57.539186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:03.007141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:08.293320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:13.357729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:18.770844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:24.238021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:28.958977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:34.223342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:39.602863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:44.540029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:49.395261image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:54.780070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:59.583658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:04.692465image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:10.117406image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:15.040423image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:19.584199image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:24.507859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:28.642219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:32.989225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:38.614685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:43.485749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:48.167746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:53.455431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:59.056122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:52.976503image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:57.742631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:03.260941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:08.505077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:13.537250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:18.982988image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:24.399707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:29.170660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:34.429662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:39.831908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:44.699922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:49.578633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:54.979568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:59.721452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:04.904678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:10.330898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:15.247931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:19.735974image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:24.683512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:28.789797image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:33.197698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:38.811855image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:43.609890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:48.363966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:53.668120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:59.191210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:53.130943image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:57.912156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:03.475846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:08.698127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:13.755786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:19.213665image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:24.552744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:29.302664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:34.630219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:40.005960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:44.875498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:49.796234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:55.118504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:59.909105image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:05.107088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:10.535762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:15.409467image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:19.976578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:24.848136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:28.932853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:33.395051image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:38.971459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:43.831051image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:48.560205image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:53.908034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:59.381054image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:53.307900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:58.153650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:03.726185image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:08.880254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:13.953713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:19.442518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:24.705840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:29.473478image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:34.861736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:40.204867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:45.037184image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:50.050789image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:55.356708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:00.069219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:05.307347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:10.674301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:15.572532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:20.167195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:25.043110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:29.084578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:33.605031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:39.144612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:43.980747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:48.763552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:54.130840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:59.513494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:53.510060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:58.320996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:03.982703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:09.041243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:14.156446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:19.662188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:24.853149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:29.635015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:35.072174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:40.368265image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:45.236884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:50.352454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:55.513346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:00.221653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:05.535079image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:10.801697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:15.753486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:20.357121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:25.202427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:29.218228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:33.757488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:39.382066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:44.191815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:48.943531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:54.387676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:59.675210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:53.638144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:58.487391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:04.391727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:09.235870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:14.298523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:19.865631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:25.038315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:29.802864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:35.331203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:40.529935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:45.377744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:50.534139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:55.686746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:00.371024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:05.752931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:10.945660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:15.924729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:20.509020image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:25.396704image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:29.318106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:33.880816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:39.597753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:44.406189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:49.113914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:54.596685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:59.833662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:53.842480image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:58.632552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:04.566527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:09.404217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:14.506176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:20.099714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:25.194703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:30.012321image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:35.568267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:40.712005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:45.551028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:50.755179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:55.849074image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:00.496035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:05.991286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:11.145773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:16.083285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:20.741914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:25.588311image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:29.535567image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:34.049803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:39.827276image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:44.571427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:49.353656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:54.805814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:00.028489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:54.057490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:58.793047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:04.754372image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:09.672369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:14.702295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:20.238270image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:25.352901image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:30.212912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:35.804947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:40.879529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:45.719479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:50.942246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:56.033994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:00.718430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:06.172306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:11.368488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:16.276810image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:20.892976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:25.741669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:29.675524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:34.209557image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:40.138537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:44.750127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:49.558532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:55.035249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:00.207599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:54.230749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:59.039601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:05.006522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:09.805061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:14.923301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:20.393370image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:25.512945image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:30.436519image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:36.056397image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:41.051511image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:45.936484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:51.133200image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:56.243487image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:00.973108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:06.425794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:11.520591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:16.427548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:21.087670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:25.894939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:29.800924image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:34.382632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:40.366739image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:44.880053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:49.738898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:55.286772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:00.395737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:54.387053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:59.162140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:05.198468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:10.036466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:15.085113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:20.614454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:25.687459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:30.581642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:36.224985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:41.239331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:46.115030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:51.355917image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:56.419218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:01.211592image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:06.676300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:11.690353image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:16.566428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:21.293192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:26.035430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:30.014530image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:34.614287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:40.528055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:45.064999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:49.926395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:55.496621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:00.533978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:54.618013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:59.364840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:05.412142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:10.235491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:15.315405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:20.832233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:25.873286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:30.751192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:36.380641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:41.462326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:46.326647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:51.577149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:56.576955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:01.465400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:06.925948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:11.879547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:16.737410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:21.467948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:26.197493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:30.207843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:34.759831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:40.701709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:45.253531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:50.084637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:55.706140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:00.714862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:54.756321image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:59.527020image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:05.573011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:10.426317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:15.518043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:21.004752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:26.060627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:30.911354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:36.524557image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:41.617373image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:46.506258image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:51.752834image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:56.776614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:01.690251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:07.112306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:12.018874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:16.909309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:21.687408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:26.325605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:30.356235image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:34.912624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:40.899123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:45.384310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:50.296507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:55.928967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:00.864003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:54.876871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:59.660376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:05.772339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:10.572770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:15.672328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:21.144677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:26.222966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:31.057025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:36.683351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:41.800943image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:46.674882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:51.909332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:56.942112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:01.864660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:07.319509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:12.185826image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:17.088297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:21.894043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:26.456164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:30.522759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:35.098066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:41.047390image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:45.533744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:50.488997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:56.117971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:01.059433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:55.063154image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:59.849536image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:05.941792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:10.801199image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:15.869643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:21.333765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:26.385583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:31.199921image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:36.846454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:41.988159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:46.849410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:52.079843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:57.081732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:02.070840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:07.481742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:12.325140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:17.252585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:22.079308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:26.638124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:30.721444image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:35.223249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:41.222597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:45.675046image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:50.618645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:56.337557image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:01.270285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:55.192511image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:00.069442image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:06.145970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:10.941287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:16.025975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:21.505604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:26.518624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:31.371863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:37.035206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:42.190335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:46.988451image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:52.263514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:57.221506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:02.254705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:07.737320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:12.452342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:17.411210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:22.254626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:26.811393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:30.831052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:35.452357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:41.394339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:45.801898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:50.744777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:56.538372image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:01.453806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:55.356384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:00.273531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:06.329175image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:11.130874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:16.230402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:21.706296image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:26.708037image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:31.538031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:37.565181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:42.379795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:47.199181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:52.449244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:57.410823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:02.436647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:07.971632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:12.589333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:17.599694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:22.471505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:26.950481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:30.982811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:35.621092image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:41.590191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:46.015234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:50.993884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:56.703223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:01.627642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:55.519946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:00.533963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:06.565162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:11.364544image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:16.442418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:21.929072image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:26.890568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:31.729104image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:37.770892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:42.561305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:47.367836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:52.610081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:57.670221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:02.627754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:08.192292image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:12.808826image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:17.770725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:22.640337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:27.093275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:31.115845image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:35.822812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:41.798083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:46.211721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:51.258096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:56.896959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:01.770186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:55.660179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:00.782836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:06.724053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:11.561707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:16.656843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:22.122644image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:27.077358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:31.926874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:37.972959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:42.793800image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:47.529174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:52.738214image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:57.803886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:02.753007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:08.381252image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:12.957070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:17.935119image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:22.847483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:27.252876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:31.255806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:36.006287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:41.940442image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:46.419278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:51.410729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:57.065665image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:01.952622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:55.828575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:01.015188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:06.893231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:11.722667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:16.890818image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:22.315103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:27.295437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:32.204671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:38.180525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:43.085748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:47.700634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:52.926300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:57.976544image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:02.937612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:08.555621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:13.174731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:18.097950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:23.044277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:27.379086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:31.462307image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:36.215579image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:42.088335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:46.615931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:51.586484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:57.273256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:02:02.097368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T13:59:56.117559image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:01.246326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:07.121935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:11.923933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:17.088090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:22.505647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:27.506904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:32.460737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:38.359589image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:43.279881image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:47.907551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:53.108467image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:00:58.156937image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:03.122193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:08.737475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:13.335766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:18.354055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:23.230950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:27.504435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:31.604912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:36.411596image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:42.242577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:46.770872image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:51.774483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T14:01:57.424944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-05-15T14:02:33.551724image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
- CASE Tool UsedExternal (EEF) - Data Quality RatingExternal (EEF) - Industry SectorISBSG Project IDPeople (PRF) - BA team experience 1 to 3 yrPeople (PRF) - BA team experience <1 yrPeople (PRF) - BA team experience >3 yrPeople (PRF) - IT experience 1 to 3 yrPeople (PRF) - IT experience 3 to 9 yrPeople (PRF) - IT experience <1 yrPeople (PRF) - IT experience <3 yrPeople (PRF) - IT experience >3 yrPeople (PRF) - IT experience >9 yrPeople (PRF) - Personnel changesPeople (PRF) - Project manage changesPeople (PRF) - Project manage experiencePeople (PRF) - Project user involvementProcess (PMF) - Development MethodologiesProcess (PMF) - DocsProject (PRF) - Application GroupProject (PRF) - Cost currencyProject (PRF) - Currency multipleProject (PRF) - Defect DensityProject (PRF) - Development TypeProject (PRF) - Functional SizeProject (PRF) - Manpower Delivery RateProject (PRF) - Max Team SizeProject (PRF) - Normalised Level 1 PDR (ufp)Project (PRF) - Normalised PDR (ufp)Project (PRF) - Normalised Work EffortProject (PRF) - Normalised Work Effort Level 1Project (PRF) - Project Elapsed TimeProject (PRF) - Relative SizeProject (PRF) - Speed of DeliveryProject (PRF) - Team Size GroupProject (PRF) - Total project costProject (PRF) - Year of ProjectTech (TF) - ArchitectureTech (TF) - Client/Server DescriptionTech (TF) - DBMS UsedTech (TF) - Development PlatformTech (TF) - Language TypeTech (TF) - Server RolesTech (TF) - Tools UsedTech (TF) - Type of ServerTech (TF) - Web Development
- CASE Tool Used1.0000.0980.2870.0260.0000.0000.0000.0000.0910.0000.1940.0000.0990.1090.1490.2120.4050.5280.5470.0820.3880.3950.1160.0440.0400.1010.0340.0000.0000.0270.0480.0820.0750.0610.1060.0940.3190.1930.6360.1340.2200.1340.8170.3770.5250.153
External (EEF) - Data Quality Rating0.0981.0000.3360.0310.0730.0000.1260.1700.1640.1050.3860.0000.0210.0820.0170.1200.4000.7750.5740.1060.3850.1980.0000.0940.0320.0000.2370.0390.0370.0000.0140.1220.1580.0060.2750.0940.3720.2320.8070.0000.1960.1590.8150.3170.7040.000
External (EEF) - Industry Sector0.2870.3361.0000.0160.2260.0000.1930.3640.3000.4640.2480.2140.1190.1770.0680.1030.3410.3200.3190.2130.3810.4630.2490.2990.0720.1090.2220.1360.1390.0620.0630.1430.1980.0490.2000.0470.3120.3110.3080.1910.2380.1500.4350.1940.4360.059
ISBSG Project ID0.0260.0310.0161.000-0.057-0.118-0.046-0.172-0.016-0.0850.056-0.073-0.067-0.0080.0000.1170.0480.0000.0130.0000.0000.106-0.0140.0110.0260.051-0.009-0.042-0.0340.000-0.0070.0020.0000.0200.000-0.0090.0000.0000.0170.0100.0000.0000.0380.0080.0610.000
People (PRF) - BA team experience 1 to 3 yr0.0000.0730.226-0.0571.0000.1460.1590.6910.3120.1960.3460.1850.0650.0630.034-0.0700.4700.6800.2070.0000.2241.000-0.0190.0000.198-0.0460.2990.1020.1150.2340.2180.0270.0730.1690.5100.2300.0790.0000.0000.0000.1160.0000.594-0.0020.5491.000
People (PRF) - BA team experience <1 yr0.0000.0000.000-0.1180.1461.000-0.2030.3680.2170.5190.283-0.0270.2240.3280.318-0.1470.0000.2900.0610.2860.3001.000-0.0410.1100.248-0.0760.2990.1720.1740.3450.3370.1250.0730.1550.361-0.073-0.0230.0000.0000.0000.0980.0000.0000.1820.1371.000
People (PRF) - BA team experience >3 yr0.0000.1260.193-0.0460.159-0.2031.0000.3700.1730.217-0.1610.7380.5680.1680.3040.1630.1950.4170.1670.0840.0610.0310.0640.1250.149-0.4070.5230.2660.2770.3120.3050.2850.107-0.0680.3200.3900.1760.0690.0000.0950.1210.0000.000-0.0960.0001.000
People (PRF) - IT experience 1 to 3 yr0.0000.1700.364-0.1720.6910.3680.3701.000NaN0.558NaN0.126NaN0.2330.2570.1560.0000.808-0.6220.0000.0000.000-0.0900.0000.368-0.3440.6960.4690.4280.4960.5430.2180.0590.1390.6510.3920.5580.1760.0001.0000.2460.1300.0000.1060.0001.000
People (PRF) - IT experience 3 to 9 yr0.0910.1640.300-0.0160.3120.2170.173NaN1.000NaN0.203NaN0.0190.2030.142-0.0330.0000.4180.1120.0000.1331.0000.1350.0000.281-0.1940.4460.2640.2640.4250.4260.1510.1500.1180.5790.340-0.0520.1920.0000.0000.1020.0000.0000.0870.0001.000
People (PRF) - IT experience <1 yr0.0000.1050.464-0.0850.1960.5190.2170.558NaN1.000NaN0.096NaN0.3270.0000.1580.0000.479-0.4650.0460.4410.000-0.1100.0000.402-0.2830.6630.3260.3320.4650.4850.2040.1280.2320.4090.3490.3510.4020.0001.0000.2760.2300.1860.3170.3051.000
People (PRF) - IT experience <3 yr0.1940.3860.2480.0560.3460.283-0.161NaN0.203NaN1.000NaN-0.2140.0560.000-0.2120.0000.5920.0600.0000.0001.000-0.0770.2610.1830.0680.1600.0340.0320.2040.196-0.1000.1120.2240.442-0.226-0.0940.0000.0000.0000.0000.0000.0000.0410.0001.000
People (PRF) - IT experience >3 yr0.0000.0000.214-0.0730.185-0.0270.7380.126NaN0.096NaN1.000NaN0.1690.0000.0050.0000.000-0.3820.0000.1730.000-0.0760.0000.281-0.5680.7140.6700.6110.5570.5550.3740.0000.0080.7450.6650.3150.0000.0001.0000.0000.0000.0000.3150.0001.000
People (PRF) - IT experience >9 yr0.0990.0210.119-0.0670.0650.2240.568NaN0.019NaN-0.214NaN1.0000.2090.2820.1290.0000.6370.0340.0000.0000.0000.1420.0660.166-0.4040.5880.3440.3440.3840.3850.2250.1460.0140.3710.1020.0200.0490.0000.1010.2260.0000.000-0.1320.0001.000
People (PRF) - Personnel changes0.1090.0820.177-0.0080.0630.3280.1680.2330.2030.3270.0560.1690.2091.0000.3180.0540.0000.5000.0260.0000.3400.296-0.1220.090-0.041-0.2660.3200.2400.1520.0760.3360.1720.144-0.1770.3640.4400.1480.0000.0000.0000.0800.0000.0000.0310.0361.000
People (PRF) - Project manage changes0.1490.0170.0680.0000.0340.3180.3040.2570.1420.0000.0000.0000.2820.3181.0000.0000.1340.3150.0000.0320.5660.0000.0000.0680.0870.0000.1140.0000.0000.1090.2930.3030.0430.0000.1600.0830.2150.0470.0000.0870.1010.2160.4690.0000.1981.000
People (PRF) - Project manage experience0.2120.1200.1030.117-0.070-0.1470.1630.156-0.0330.158-0.2120.0050.1290.0540.0001.0000.1570.0640.1550.1180.2270.521-0.1080.129-0.0060.0420.039-0.091-0.030-0.018-0.0380.0720.117-0.0490.0000.1130.1500.0000.0000.0000.0770.0000.497-0.0290.5761.000
People (PRF) - Project user involvement0.4050.4000.3410.0480.4700.0000.1950.0000.0000.0000.0000.0000.0000.0000.1340.1571.0000.5000.6410.1320.1310.0000.0000.2240.0290.1220.0001.0001.0000.0560.0690.1670.1610.0000.1420.0310.4130.1490.3501.0000.4840.1180.5740.2450.4481.000
Process (PMF) - Development Methodologies0.5280.7750.3200.0000.6800.2900.4170.8080.4180.4790.5920.0000.6370.5000.3150.0640.5001.0000.6490.2020.6020.0000.4290.3660.0120.0000.7740.0000.0000.3080.2960.1780.1660.0000.4240.0000.3600.4420.5020.0000.4230.2490.6780.4380.5120.000
Process (PMF) - Docs0.5470.5740.3190.0130.2070.0610.167-0.6220.112-0.4650.060-0.3820.0340.0260.0000.1550.6410.6491.0000.1590.3340.5690.0960.1860.2840.297-0.268-0.217-0.1670.0560.0490.1070.1100.1610.1670.052-0.1770.2570.6570.0880.2280.1200.7620.5710.6460.000
Project (PRF) - Application Group0.0820.1060.2130.0000.0000.2860.0840.0000.0000.0460.0000.0000.0000.0000.0320.1180.1320.2020.1591.0000.3840.3580.0000.0980.0000.1010.0000.0000.0000.0000.0000.0000.0540.0000.0380.0000.1670.1120.4100.0490.1010.0000.4220.0350.2980.096
Project (PRF) - Cost currency0.3880.3850.3810.0000.2240.3000.0610.0000.1330.4410.0000.1730.0000.3400.5660.2270.1310.6020.3340.3841.0000.4050.0000.3470.1630.1870.1130.0000.0000.0000.0000.4590.1420.3520.2010.1380.3670.2410.2270.0000.3260.6400.5640.3600.5771.000
Project (PRF) - Currency multiple0.3950.1980.4630.1061.0001.0000.0310.0001.0000.0001.0000.0000.0000.2960.0000.5210.0000.0000.5690.3580.4051.0001.0000.0820.0000.3980.0000.0000.0000.0000.0000.0000.1130.0000.3350.1230.1470.1550.0000.9890.0000.0961.0000.3891.0001.000
Project (PRF) - Defect Density0.1160.0000.249-0.014-0.019-0.0410.064-0.0900.135-0.110-0.077-0.0760.142-0.1220.000-0.1080.0000.4290.0960.0000.0001.0001.0000.0000.155-0.0620.0610.1180.0510.1590.1630.1320.0320.0780.0000.095-0.2770.3310.4710.0000.0120.0520.539-0.0020.2551.000
Project (PRF) - Development Type0.0440.0940.2990.0110.0000.1100.1250.0000.0000.0000.2610.0000.0660.0900.0680.1290.2240.3660.1860.0980.3470.0820.0001.0000.1410.0910.1750.0470.0440.0790.0830.1420.2920.0710.2060.0000.2630.0650.3530.0540.1940.0580.3680.0910.3280.036
Project (PRF) - Functional Size0.0400.0320.0720.0260.1980.2480.1490.3680.2810.4020.1830.2810.166-0.0410.087-0.0060.0290.0120.2840.0000.1630.0000.1550.1411.0000.629-0.100-0.368-0.3350.5950.5630.2620.5610.7650.0610.465-0.3770.0000.1770.0000.0000.0410.2190.2150.0841.000
Project (PRF) - Manpower Delivery Rate0.1010.0000.1090.051-0.046-0.076-0.407-0.344-0.194-0.2830.068-0.568-0.404-0.2660.0000.0420.1220.0000.2970.1010.1870.398-0.0620.0910.6291.000-0.731-0.858-0.865-0.226-0.235-0.5160.2110.8430.136-0.143-0.5660.0800.2080.0000.1470.0700.1400.2110.0001.000
Project (PRF) - Max Team Size0.0340.2370.222-0.0090.2990.2990.5230.6960.4460.6630.1600.7140.5880.3200.1140.0390.0000.774-0.2680.0000.1130.0000.0610.175-0.100-0.7311.0000.6930.7050.5880.5900.4390.128-0.3120.6060.5590.4260.0100.0000.0000.0450.0000.889-0.1510.4140.000
Project (PRF) - Normalised Level 1 PDR (ufp)0.0000.0390.136-0.0420.1020.1720.2660.4690.2640.3260.0340.6700.3440.2400.000-0.0911.0000.000-0.2170.0000.0000.0000.1180.047-0.368-0.8580.6931.0000.9890.4720.4940.3400.119-0.5530.1890.3990.2440.0000.4700.1300.0000.0080.450-0.1261.0001.000
Project (PRF) - Normalised PDR (ufp)0.0000.0370.139-0.0340.1150.1740.2770.4280.2640.3320.0320.6110.3440.1520.000-0.0301.0000.000-0.1670.0000.0000.0000.0510.044-0.335-0.8650.7050.9891.0000.4890.4900.3270.119-0.5330.1890.3710.2070.0000.4700.1320.0000.0000.467-0.0941.0001.000
Project (PRF) - Normalised Work Effort0.0270.0000.0620.0000.2340.3450.3120.4960.4250.4650.2040.5570.3840.0760.109-0.0180.0560.3080.0560.0000.0000.0000.1590.0790.595-0.2260.5880.4720.4891.0000.9880.5720.1730.2310.1300.934-0.1510.0790.3330.0000.0210.0000.3360.1380.0930.000
Project (PRF) - Normalised Work Effort Level 10.0480.0140.063-0.0070.2180.3370.3050.5430.4260.4850.1960.5550.3850.3360.293-0.0380.0690.2960.0490.0000.0000.0000.1630.0830.563-0.2350.5900.4940.4900.9881.0000.5740.1780.2070.1300.938-0.1090.0860.3170.0000.0190.0000.3230.1380.0000.000
Project (PRF) - Project Elapsed Time0.0820.1220.1430.0020.0270.1250.2850.2180.1510.204-0.1000.3740.2250.1720.3030.0720.1670.1780.1070.0000.4590.0000.1320.1420.262-0.5160.4390.3400.3270.5720.5741.0000.154-0.3450.1740.446-0.0430.0880.2050.0000.0760.2040.2240.1850.0150.134
Project (PRF) - Relative Size0.0750.1580.1980.0000.0730.0730.1070.0590.1500.1280.1120.0000.1460.1440.0430.1170.1610.1660.1100.0540.1420.1130.0320.2920.5610.2110.1280.1190.1190.1730.1780.1541.0000.3200.2050.3390.1570.0500.1800.0000.0810.0400.2080.0660.1111.000
Project (PRF) - Speed of Delivery0.0610.0060.0490.0200.1690.155-0.0680.1390.1180.2320.2240.0080.014-0.1770.000-0.0490.0000.0000.1610.0000.3520.0000.0780.0710.7650.843-0.312-0.553-0.5330.2310.207-0.3450.3201.0000.0740.189-0.3110.0000.3810.0000.0280.0000.3620.0720.1311.000
Project (PRF) - Team Size Group0.1060.2750.2000.0000.5100.3610.3200.6510.5790.4090.4420.7450.3710.3640.1600.0000.1420.4240.1670.0380.2010.3350.0000.2060.0610.1360.6060.1890.1890.1300.1300.1740.2050.0741.0000.1140.2210.1520.2100.0000.1520.0450.4030.1300.2020.000
Project (PRF) - Total project cost0.0940.0940.047-0.0090.230-0.0730.3900.3920.3400.349-0.2260.6650.1020.4400.0830.1130.0310.0000.0520.0000.1380.1230.0950.0000.465-0.1430.5590.3990.3710.9340.9380.4460.3390.1890.1141.000-0.0300.0000.0000.0000.0000.0000.0000.0550.0001.000
Project (PRF) - Year of Project0.3190.3720.3120.0000.079-0.0230.1760.558-0.0520.351-0.0940.3150.0200.1480.2150.1500.4130.360-0.1770.1670.3670.147-0.2770.263-0.377-0.5660.4260.2440.207-0.151-0.109-0.0430.157-0.3110.221-0.0301.0000.3300.4190.1610.2830.1180.561-0.2160.4290.000
Tech (TF) - Architecture0.1930.2320.3110.0000.0000.0000.0690.1760.1920.4020.0000.0000.0490.0000.0470.0000.1490.4420.2570.1120.2410.1550.3310.0650.0000.0800.0100.0000.0000.0790.0860.0880.0500.0000.1520.0000.3301.0000.9860.0280.4240.1610.9670.1670.8850.000
Tech (TF) - Client/Server Description0.6360.8070.3080.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3500.5020.6570.4100.2270.0000.4710.3530.1770.2080.0000.4700.4700.3330.3170.2050.1800.3810.2100.0000.4190.9861.0000.2720.6200.4870.9950.5280.3490.992
Tech (TF) - DBMS Used0.1340.0000.1910.0100.0000.0000.0951.0000.0001.0000.0001.0000.1010.0000.0870.0001.0000.0000.0880.0490.0000.9890.0000.0540.0000.0000.0000.1300.1320.0000.0000.0000.0000.0000.0000.0000.1610.0280.2721.0000.0980.0080.2940.0801.0000.000
Tech (TF) - Development Platform0.2200.1960.2380.0000.1160.0980.1210.2460.1020.2760.0000.0000.2260.0800.1010.0770.4840.4230.2280.1010.3260.0000.0120.1940.0000.1470.0450.0000.0000.0210.0190.0760.0810.0280.1520.0000.2830.4240.6200.0981.0000.4660.7060.1680.5370.069
Tech (TF) - Language Type0.1340.1590.1500.0000.0000.0000.0000.1300.0000.2300.0000.0000.0000.0000.2160.0000.1180.2490.1200.0000.6400.0960.0520.0580.0410.0700.0000.0080.0000.0000.0000.2040.0400.0000.0450.0000.1180.1610.4870.0080.4661.0000.4340.0290.2940.000
Tech (TF) - Server Roles0.8170.8150.4350.0380.5940.0000.0000.0000.0000.1860.0000.0000.0000.0000.4690.4970.5740.6780.7620.4220.5641.0000.5390.3680.2190.1400.8890.4500.4670.3360.3230.2240.2080.3620.4030.0000.5610.9670.9950.2940.7060.4341.0000.5510.9870.985
Tech (TF) - Tools Used0.3770.3170.1940.008-0.0020.182-0.0960.1060.0870.3170.0410.315-0.1320.0310.000-0.0290.2450.4380.5710.0350.3600.389-0.0020.0910.2150.211-0.151-0.126-0.0940.1380.1380.1850.0660.0720.1300.055-0.2160.1670.5280.0800.1680.0290.5511.0000.2330.135
Tech (TF) - Type of Server0.5250.7040.4360.0610.5490.1370.0000.0000.0000.3050.0000.0000.0000.0360.1980.5760.4480.5120.6460.2980.5771.0000.2550.3280.0840.0000.4141.0001.0000.0930.0000.0150.1110.1310.2020.0000.4290.8850.3491.0000.5370.2940.9870.2331.0001.000
Tech (TF) - Web Development0.1530.0000.0590.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0961.0001.0001.0000.0361.0001.0000.0001.0001.0000.0000.0000.1341.0001.0000.0001.0000.0000.0000.9920.0000.0690.0000.9850.1351.0001.000

Missing values

2025-05-15T14:02:02.600759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-15T14:02:03.378655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-15T14:02:05.588377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ISBSG Project IDExternal (EEF) - Data Quality RatingProject (PRF) - Year of ProjectExternal (EEF) - Industry SectorExternal (EEF) - Organisation TypeProject (PRF) - Application GroupProject (PRF) - Application TypeProject (PRF) - Development TypeTech (TF) - Development PlatformTech (TF) - Language TypeTech (TF) - Primary Programming LanguageProject (PRF) - Functional SizeProject (PRF) - Relative SizeProject (PRF) - Normalised Work Effort Level 1Project (PRF) - Normalised Work EffortProject (PRF) - Normalised Level 1 PDR (ufp)Project (PRF) - Normalised PDR (ufp)Project (PRF) - Defect DensityProject (PRF) - Speed of DeliveryProject (PRF) - Manpower Delivery RateProject (PRF) - Project Elapsed TimeProject (PRF) - Team Size GroupProject (PRF) - Max Team Size- CASE Tool UsedProcess (PMF) - Development MethodologiesProcess (PMF) - Prototyping UsedProcess (PMF) - DocsTech (TF) - ArchitectureTech (TF) - Client Server?Tech (TF) - Client RolesTech (TF) - Server RolesTech (TF) - Type of ServerTech (TF) - Client/Server DescriptionTech (TF) - Web DevelopmentTech (TF) - DBMS UsedTech (TF) - Tools UsedPeople (PRF) - Project user involvementPeople (PRF) - BA team experience <1 yrPeople (PRF) - BA team experience 1 to 3 yrPeople (PRF) - BA team experience >3 yrPeople (PRF) - IT experience <1 yrPeople (PRF) - IT experience 1 to 3 yrPeople (PRF) - IT experience >3 yrPeople (PRF) - IT experience <3 yrPeople (PRF) - IT experience 3 to 9 yrPeople (PRF) - IT experience >9 yrPeople (PRF) - Project manage experiencePeople (PRF) - Project manage changesPeople (PRF) - Personnel changesProject (PRF) - Total project costProject (PRF) - Cost currencyProject (PRF) - Currency multiple
010003B2015CommunicationTelecommunications;Business ApplicationOnline. eSales;EnhancementMultiNaNNaN67.0S741.074111.111.1NaN15.2NaN4.4NaNNaNNaNWaterfall (incl Linear Processing & SSADM);Yes3NaNNaNNaNNaNNaNNaNWebNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
110011B1996ConstructionConstruction;Business ApplicationStock control & order processing;New DevelopmentMulti4GLAccess443.0M2856.08561.91.9NaN170.4NaN2.6NaNNaNNoWaterfall (incl Linear Processing & SSADM);NaN3YesNaNNaNC/S;NaNC/S;NaNYes0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
210012B2002Wholesale & RetailBilling;Business ApplicationBilling;EnhancementNaN3GLCOBOL76.0S1100.0110014.514.5NaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
310014B2004NaNNaNNaNNaNEnhancementNaNNaNNaN3.0XXS28.0289.39.3NaNNaNNaNNaNNaNNaNNaNNaNNaN2YesNaNNaNC/S;NaNC/S;NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
410015B2000Wholesale & RetailWholesale & Retail Trade;Business ApplicationManagement Information System;EnhancementMF3GLCOBOL382.0M2NaN23913NaN62.6NaN127.3NaN3.0NaNNaNYesNaNNaN4YesNaNNaNNaNNaNNaNNaNYes2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
510019B2014CommunicationTelecommunications;Business ApplicationData Warehouse system;EnhancementMulti3GLShell98.0S324.03243.33.3NaN22.8NaN4.3NaNNaNNaNWaterfall (incl Linear Processing & SSADM);Yes3NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
610026B2000InsuranceInsurance;Business ApplicationSales contact management;New DevelopmentMF3GLJava620.0M218160.01816029.329.3NaN88.6NaN7.0NaNNaNNoWaterfall (incl Linear Processing & SSADM);NaN3Don't KnowNaNNaNNaNNaNNaNNaNYes0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
710028B2015CommunicationTelecommunications;Business ApplicationCustomer relationship management;EnhancementMulti4GLSiebel138.0M12954.0295421.421.4NaN28.2NaN4.9NaNNaNNaNWaterfall (incl Linear Processing & SSADM);Yes3NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
810029B2004BankingBanking;Business ApplicationFinancial transaction process/accounting;New DevelopmentMF3GLCOBOL297.0M18186.0818627.627.6NaNNaNNaNNaNNaNNaNNaNNaNNaN3NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
910033B2012Medical & Health CareMedical and Health Care;NaNnot recorded;EnhancementNaNNaNNaN162.0M192380.092380570.2570.2NaN8.10.020.0101+253.0NaNNaNNaN2NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4071933.0United States, dollarNo
ISBSG Project IDExternal (EEF) - Data Quality RatingProject (PRF) - Year of ProjectExternal (EEF) - Industry SectorExternal (EEF) - Organisation TypeProject (PRF) - Application GroupProject (PRF) - Application TypeProject (PRF) - Development TypeTech (TF) - Development PlatformTech (TF) - Language TypeTech (TF) - Primary Programming LanguageProject (PRF) - Functional SizeProject (PRF) - Relative SizeProject (PRF) - Normalised Work Effort Level 1Project (PRF) - Normalised Work EffortProject (PRF) - Normalised Level 1 PDR (ufp)Project (PRF) - Normalised PDR (ufp)Project (PRF) - Defect DensityProject (PRF) - Speed of DeliveryProject (PRF) - Manpower Delivery RateProject (PRF) - Project Elapsed TimeProject (PRF) - Team Size GroupProject (PRF) - Max Team Size- CASE Tool UsedProcess (PMF) - Development MethodologiesProcess (PMF) - Prototyping UsedProcess (PMF) - DocsTech (TF) - ArchitectureTech (TF) - Client Server?Tech (TF) - Client RolesTech (TF) - Server RolesTech (TF) - Type of ServerTech (TF) - Client/Server DescriptionTech (TF) - Web DevelopmentTech (TF) - DBMS UsedTech (TF) - Tools UsedPeople (PRF) - Project user involvementPeople (PRF) - BA team experience <1 yrPeople (PRF) - BA team experience 1 to 3 yrPeople (PRF) - BA team experience >3 yrPeople (PRF) - IT experience <1 yrPeople (PRF) - IT experience 1 to 3 yrPeople (PRF) - IT experience >3 yrPeople (PRF) - IT experience <3 yrPeople (PRF) - IT experience 3 to 9 yrPeople (PRF) - IT experience >9 yrPeople (PRF) - Project manage experiencePeople (PRF) - Project manage changesPeople (PRF) - Personnel changesProject (PRF) - Total project costProject (PRF) - Cost currencyProject (PRF) - Currency multiple
704832748B2001ManufacturingManufacturing;NaNNaNEnhancementMF3GLPL/I902.0M23748.0224784.224.90.053.13.117.015-2017.0YesNaNNaN2NaNNaNNaNNaNNaNNaNNaNYes4.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaN
704932753B2015CommunicationTelecommunications;Business ApplicationCustomer relationship management;EnhancementMultiNaNNaN63.0S1005.0100516.016.0NaNNaNNaNNaNNaNNaNNaNWaterfall (incl Linear Processing & SSADM);Yes3NaNNaNNaNNaNNaNNaNNaNNaN5.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
705032754B2015CommunicationTelecommunications;Business ApplicationOnline. eSales;EnhancementMultiNaNNaN74.0S1907.0190725.825.8NaN15.1NaN4.9NaNNaNNaNWaterfall (incl Linear Processing & SSADM);Yes3NaNNaNNaNNaNNaNNaNWebNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
705132755B2002FinancialFinancial, Property & Business Services;Business ApplicationFinancial transaction process/accounting;EnhancementNaNNaNNaN197.0M11214.012146.26.2NaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaNNaNNaNNaNNaNNaNYes1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
705232756B2008GovernmentGovernment;Defence;Business ApplicationFinancial transaction process/accounting;EnhancementMF3GLCOBOL23.0XS224.02249.79.743.51.8NaN12.5NaNNaNNaNNaNNaN2NoNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20290.0United States, dollarNo
705332757B1998NaNNaNNaNNaNNew DevelopmentNaNNaNNaN291.0M1710.09602.43.3NaN145.548.52.03-43.0Don't KnowNaNNaN4NaNNaNNaNNaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
705432758B1995GovernmentGovernment;Business ApplicationManagement Information System;EnhancementMF3GLCOBOL119.0M1NaN2312NaN19.4NaN11.9NaN10.0NaNNaNNoNaNNaN5NaNNaNNaNNaNNaNNaNNaNYes6.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
705532762B2014CommunicationTelecommunications;Business ApplicationCustomer relationship management;EnhancementMulti3GLJava50.0S556.055611.111.1NaN11.6NaN4.3NaNNaNNaNWaterfall (incl Linear Processing & SSADM);Yes3NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
705632766A2000GovernmentPublic Administration;Business ApplicationElectronic Data Interchange;New DevelopmentPC3GLC45.0S60.0801.31.8NaN45.015.01.03-43.0NoNaNNaN5NoNaNNaNDynamic Link Library;NaNDynamic Link Library;NaNYes4.0No2.01.00.00.03.0NaNNaNNaNNaN17.00.00.03975.0Canada, dollarNaN
705732767B2009ManufacturingManufacturing;Business ApplicationCars selling;EnhancementMulti3GLJava95.0SNaN1449NaN15.30.015.8NaN6.0NaNNaNYesNaNNaN8YesNaNNaNClient server;Client server;NaNNaNYes2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN